# Chatbots in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug
Development with ChatGPT

Rui Wang1, Hongsong Feng1, and Guo-Wei Wei1,2,3111 Corresponding author.
Email<EMAIL_ADDRESS>
1 Department of Mathematics,
Michigan State University, MI 48824, USA.
2 Department of Electrical and Computer Engineering,
Michigan State University, MI 48824, USA.
3 Department of Biochemistry and Molecular Biology,
Michigan State University, MI 48824, USA.

###### Abstract

The birth of ChatGPT, a cutting-edge language model chatbot developed by
OpenAI, ushered in a new era in AI, and this paper vividly showcases its
innovative application within the field of drug discovery. Focused
specifically on developing anti-cocaine addiction drugs, the study employs
GPT-4 as a virtual guide, offering strategic and methodological insights to
researchers working on generative models for drug candidates. The primary
objective is to generate optimal drug-like molecules with desired properties.
By leveraging the capabilities of ChatGPT, the study introduces a novel
approach to the drug discovery process. This symbiotic partnership between AI
and researchers transforms how drug development is approached. Chatbots become
facilitators, steering researchers towards innovative methodologies and
productive paths for creating effective drug candidates. This research sheds
light on the collaborative synergy between human expertise and AI assistance,
wherein ChatGPT’s cognitive abilities enhance the design and development of
potential pharmaceutical solutions. This paper not only explores the
integration of advanced AI in drug discovery but also reimagines the landscape
by advocating for AI-powered chatbots as trailblazers in revolutionizing
therapeutic innovation.

Keywords: Drug Discovery, ChatGPT, Cocaine Addition, AutoEncoder, Langevin
Equation

##  1 Main

Chatbots represent a typical artificial intelligence system capable of
comprehending user queries and providing automated and human-like responses
[1], standing as one of the most prevalent instances of intelligent Human-
Computer Interaction (HCI) [2]. Harnessing the power of natural language
processing (NLP) and machine learning technologies, chatbots offer significant
potential in various domains, including customer service, healthcare, banking,
language translation, content writing, code debugging, and scientific
discovery, despite the relative novelty of applying chatbots in scientific
field. The advent of chatbots, especially large language models (LLMs) such as
ChatGPT developed by OpenAI in late 2022, has revolutionized scientific
discovery [3]. Firstly, ChatGPT optimizes research processes by rapidly
parsing vast amounts of literature and identifying key findings with its
built-in plugin called web browser. This can save considerable time for
researchers, thus facilitating the exploration of complex scientific problems.
Secondly, ChatGPT provides researchers with a platform to analyze data,
visualize results, convert files among various formats, and solve mathematical
problems with its built-in code interpreter. Thirdly, ChatGPT can assist in
enhancing scientific writing by providing feedback for clarity and logical
structuring of scientific content. The blend of these powerful capabilities
fosters a new era in research, improving the efficiency and accuracy of
scientific exploration in various fields, including molecular and biological
science. By expediting the pace of molecular discovery and offering novel
perspectives, chatbots such as ChatGPT, is reshaping the landscape of life
science research.

Chatbots can be applied to assist molecular science research in a variety of
ways. For example, ChatGPT has been leveraged to accurately annotate single-
cell RNA sequencing data, connecting rare cell types to their functions and
unveiling specific differentiation trajectories of cell subtypes that were
previously overlooked. [4]. This assistance by ChatGPT could potentially lead
to the discovery of key cells that disrupt differentiation pathways, offering
fresh insights into cellular biology and related diseases. Moreover, White et
al demonstrated that InstructGPT can help in writing accurate code across a
variety of topics in chemistry [5]. The application of prompt engineering
strategies further improved the accuracy of models by 30 percentage points,
significantly enhancing the efficiency and accuracy of computational chemical
studies. In addition, ChatGPT has shown potential in identifying disease-
specific agents, compounds, genes, and more. This enables faster and more
accurate pinpointing of potential targets for therapeutic intervention [6].
Futhermore, ChatGPT can generate novel compound structures that have a high
likelihood of clinical success [7] and predict the pharmacokinetic (PK),
pharmacodynamic (PD), and toxicity properties of these compounds [6]. This
capacity to predict compound behavior, which has potential to reduce the need
for expensive and time-consuming lab tests.

Moving forward to more specific challenges within molecular science, chatbots
could make significant contributions to the drug addiction treatment and
prevention, which is global health crisis. Effective strategies to combat drug
addiction often involve a combination of behavioral therapy, counseling, and
medication, all directed towards assisting individuals in regaining control of
their lives and attaining prolonged sobriety. Drug addiction is intrinsically
complex, characterized by a convergence of biological, psychological, and
social elements. These intricacies, compounded by profound neurobiological
transformations, present formidable challenges in both its understanding and
its mitigation. Chatbots, with their capabilities, could offer valuable
assistance in this domain. For example, a study by Lee et al. introduced an
”anti-drug chatbot” specifically tailored for the younger demographic. This
innovative system has the capability to discern potential risks from user
queries and directs the individual to professional consultants for further
assistance and guidance [8].

It is worth noting that machine learning (ML) and artificial intelligence (AI)
tools have been pivotal in advancing our understanding of drug addiction and
substance abuse. Gong et al., developed a data-driven and end-to-end
generative AI framework that integrates dynamic brain network modeling with
novel network architecture. This framework highlights the potential of AI in
detecting addiction-related brain circuits with dynamic properties, offering
insights into the underlying mechanisms of addiction [9]. In our prior
research, we underscored the critical roles of dopamine transporter (DAT),
serotonin transporter (SERT), and norepinephrine transporter (NET) as central
players in cocaine dependence. Leveraging machine learning algorithms, we
meticulously dissected protein-protein interaction (PPI) networks and
constructed models from extensive datasets of inhibitors. Our models
forecasted drug repurposing avenues and potential side effects, providing a
systematic protocol AI-driven framework for anti-cocaine addiction drug
development [10]. ML-based approaches have been extensive applied to drug
discovery [11, 12, 13]. Given the rise of chatbots and AI, we recognize the
promising potential of these technologies to enhance AI-driven algorithms in
drug addiction research projects.

The objective of this project is to harness the sophisticated capabilities of
ChatGPT, specifically GPT-4 equipped with multiple plugins, to promote the
development of multi-target anti-cocaine addiction drugs. In this study, we
investigate the utility of ChatGPT as a virtual assistant that offers
insightful concepts, elucidates mathematical and statistical methodologies,
and provides coding support. To optimize our anti-cocaine addiction drug
discovery project, we assign ChatGPT with three human-like personas: 1) idea
generation, 2) methodology clarification, and 3) coding assistance to
frequently assist us to develop a model that could generate potential multi-
target anti-cocaine addiction leads. Beyond these three characteristics, we
engage in regular consultations with GPT-4 on interpreting properties of
potential leads, seeking guidance on scientific writing, etc. Although the
benefits of using ChatGPT in drug discovery are significant, challenges of
ensuring the accuracy and reliability of the responses provided by ChatGPT
remain a major concern. Despite being trained on extensive datasets, ChatGPT
does not come with a guarantee of consistent precision or relevance in its
responses. As such, it is imperative for researchers to utilize ChatGPT
judiciously and always cross-reference its suggestions with authoritative
sources. ChatGPT could substantially accelerate the pace of drug discovery and
other scientific pursuits by applying it properly and wisely with a discerning
mind.

In this work, the first persona of ChatGPT is tasked with understanding
related works on AI-assisted drug addiction research, with a particular focus
on our prior projects that utilized the Generative Network Complex (GNC) [14,
15] for drug-like molecule generations. Concurrently, this persona will offer
recommendations on enhancing the GNC model mathematically and statistically,
aiming to generate anti-cocaine addiction leads targeting multiple
transporters, namely DAT, NET, and SERT. After consultation with GPT-4, we
decided to integrate stochastic-based methodologies to steer the optimization
process within the latent space of the existing GNC model. Specifically, we
employed the Langevin equation to modify the latent space vector in the
molecular generator of GNC (see Figure 1 Stochastic-based Molecular
Generator). In addition, upon advice from GPT-4, we examined the binding
affinities and other pertinent properties for multiple targets concurrently
(see Figure 1 Binding Affinities Predictors). This involved the creation of a
series of binding affinity predictors, capable of estimating potential lead
affinities to DAT, NET, and SERT simultaneously. Moreover, the second persona
of GPT-4 will act as an adept browser, facilitating our comprehension of
various mathematical and statistical principles, including Itô’s lemma, the
Wiener process, white noise, Langevin equation, Fokker-Planck equation, etc.
Furthermore, we applied the third persona of GPT-4 to provide instant coding
assistance, including debugging, generating figures, and interpreting code.
With the combined expertise of these three personas, we successfully developed
a new platform called Stochastic Generative Network Complex (SGNC) that could
generate 15 promising multi-target anti-cocaine addiction leads. The workflow
of the SGNC assited by ChatGPT can be viewed in Figure 1.

Figure 1: Workflow of the stochastic-based generative network complex (SGNC).
ChatGPT was extensively involved in the building process of the SGNC. Dark
arrows show the training process, brown arrows indicate the validation
process, and red arrows are the generation process. The SGNC comprises 4
primary structures: 1) Sequence-to-Sequence AutoEncoder (green), 2) binding
affinity predictors (yellow), 3) stochastic-based molecular generator (blue),
and 4) ADMET screening via ADMETlab 2.0 (purple).

##  2 Results

###  2.1 ChatGPT as a virtual guide in drug discovery

ChatGPT is a large language model (LLM) that has made significant strides in
research since the release of its free version, ChatGPT 3.5, on November 30,
2022. Subsequently, on March 14, 2023, OpenAI launched an upgraded version,
GPT-4, which possesses enhanced capabilities in solving complex problems with
greater accuracy and more reasonable responses compared to its predecessor.
Moreover, on May 12, 2023, OpenAI introduced web browsing and plugin features
to ChatGPT Plus users. These features enable GPT-4 to browse the internet and
utilize third-party plugins, thereby improving its ability to provide up-to-
date information and cater to queries across various platforms. The advanced
capabilities of GPT-4 have opened up new avenues for exploration in fields
that rely heavily on data analysis and artificial intelligence. In this work,
we primarily leverage GPT-4 to better assist us in an AI-assisted anti-cocaine
addiction drug discovery project. Particularly, GPT-4 will act as a tool for
digesting vast amounts of literature, advising on new research ideas,
explaining complex math-based methodologies, and improving coding efficiency.

It is worth noting that although GPT-4 has demonstrated impressive abilities
in providing reasonable responses, it is still susceptible to generating false
narratives and misinformation. Consequently, scientists cannot rely solely on
GPT-4 for their research topics. In this work, we will consistently verify the
information generated from GPT-4. This verification process involves 1) cross-
referencing with existing literature, and 2) applying our own knowledge,
expertise, and critical thinking to validate the information and insights
provided by GPT-4.

Based on this verification process, we will then decide whether to accept the
responses from GPT-4 or not. If the information aligns well with the
literature and our expertise, we will accept the responses and proceed with
the suggestions of GPT-4. Otherwise, we will reject the answer and seek
further clarification or explore alternative approaches. Through this vigilant
integration of the computational capabilities of GPT-4 with expertise of
researchers, we aim to maximize the reliability and efficacy of the research
outcomes in AI-assisted drug discovery.

###  2.2 A case study: Anti-cocaine addiction drug discovery assisted by
ChatGPT

####  2.2.1 Personifying ChatGPT: Role designation

Personification refers to the process of assigning human-like characteristics
or a persona to an AI model. In this project, we have strategically
personified ChatGPT to improve its capacity to better assist our anti-cocaine
addiction drug discovery initiative. In this project, we have tailored three
persona of ChatGPT to fit three roles within the project: 1) idea generation,
2) methodology elucidation, and 3) coding augmentation. It is worth mentioning
that we personified ChatGPT in three individual chatbox. Each individual
chatbox does not have access to acquire data from other chatbox.

For the role of idea generation, we assigned ChatGPT the 1st persona of a
professor with specific expertise in AI-assisted drug discovery, focusing
particularly on treating cocaine addiction. This persona was designed to guide
Ph.D. students and postdocs on this specific project, offering insightful
explanations, suggestions, or expert advice based on extensive knowledge and
experience in the field. We provided it with questions, scenarios, and
research plans related to the application of AI in drug discovery for treating
cocaine addiction, and instructed it to focus exclusively on the subject
matter and offer guidance as if it were mentoring in a real-life research
setting. For the first persona of ChatGPT, we have enabled three plugins:
WebPilot, ScholarAI, and AskYourPDF. These additional plugins aim to enhance
ChatGPT’s ability to comprehend the background of anti-cocaine addiction drug
discovery comprehensively. With these plugins enabled, ChatGPT is capable of
enumerating up-to-date sources on the web, as well as accessing insights from
previous works by other researchers. Complete dialogues regarding the 1st
persona of ChatGPT can be found in the Supporting Information S4.1.

List of dialogues 1 The 1st persona of ChatGPT: A professor with specific
expertise in AI-assisted drug discovery, focusing particularly on treating
cocaine addiction. Plugins: WebPilot, ScholarAI, AskYourPDF.

In order to elucidate the methodology that will be involved in this project,
we assigned the 2nd persona of ChatGPT the role of a professional researcher
who is well-versed in diffusion models and statistical methodologies. This
persona aims to provide clear explanations, insights, or recommendations in
LaTex format. This specific persona was chosen as our 1st ChatGPT persona
provided an insightful idea which based on the statistical strategies and
diffusion models (refer to Section 2.2.3 for details). Furthermore, we have
enabled three plugins (WebPilot, Link Reader, and Wolfram) for this second
persona. The choice of WebPilot and Link Reader helps ChatGPT to unlock web
sources related to statistical methods, while the inclusion of Wolfram
provides access to computational resources, mathematical tools, curated
knowledge, and real-time data through Wolfram’s software, significantly
enhancing the mathematical and statistical utility of this persona. Complete
dialogues regarding the 2nd persona of ChatGPT can be found in the Supporting
Information S4.2.

List of dialogues 2 The 2nd persona of ChatGPT: A professional research with
specific expertise in diffusion models and statistical methodologies. Plugins:
WebPilot, Link Reader, Wolfram.

We designated the third persona of ChatGPT as a Python coding specialist, with
an emphasis on artificial intelligence and figure generation. This persona is
tasked with offering lucid explanations, code snippets, and efficiency
optimization for our coding tasks. Specifically, for figure generation, we
prefer that ChatGPT utilizes Plotly, which is a Python-based plotting library.
Additionally, we have enabled three plugins for this persona: WebPilot,
ChatwithGit, and Prompt Perfect. WebPilot ensures easy access to websites
regarding coding skills, ChatwithGit enables accessibility to GitHub, and
Prompt Perfect aids in generating perfect prompts. Complete dialogues
regarding the 3rd persona of ChatGPT can be found in the Supporting
Information S4.3.

List of dialogues 3 The 3rd persona of ChatGPT: An expert in coding Python,
with a specific focus on artificial intelligence and figure generation,
preferably using Plotly. Plugins: WebPilot, ChatwithGit, Prompt Perfect.

####  2.2.2 Background comprehension: ChatGPT summary of past work

For the 1st persona of ChatGPT, we initiated the process by feeding GPT-4 with
relevant literature to ensure it has a thorough understanding of the
fundamental concepts in cocaine addiction. These concepts include
neurotransmitters, the dopamine hypothesis of addiction, the reward pathway of
the mesolimbic dopamine system, pharmacotherapy for cocaine addiction, and
machine learning approaches in cocaine addiction-related analysis.

Next, we acquainted GPT-4 with our prior research on a generative model for
the automated generation of drug-like molecules [14]. This step is crucial for
ensuring that GPT-4 is well-versed in the context of our previous work,
enabling it to provide tailored assistance that is directly aligned with our
specific objectives. In particular, we have two primary goals: 1) to apply
mathematical or statistical techniques to develop an enhanced model, building
upon our former Generative Network Complex (GNC) model [14], and 2) to refine
this model so that it is capable of generating new molecules that could bind
to multiple targets simultaneously.

To ensure that GPT-4 has effectively assimilated the background materials, we
tasked it with summarizing the main concepts of the paper we provided and
explaining the key components of the GNC model, as shown in the following
chatbox. Upon evaluation and based on our expertise, we believed that GPT-4
had successfully integrated the background materials that could assist our
project tailored to our needs. Therefore, the next step is to consult our 1st
persona of ChatGPT to provide some valuable ideas.

List of dialogues 4 The ability of GPT-4 to comprehend the background
materials of former work.

####  2.2.3 Idea generation: ChatGPT’s unique contributions

Subsequently, we engaged with GPT-4 to determine which specific component of
the GNC could be adapted for multi-target objectives (refer to 5). Then we
evaluated the feasibility of each option that GPT-4 provided, and determined
if these suggestions can be tailored to meet our specific needs/tastes. The
first suggestion from GPT-4 focuses on the multi-property optimization, which
involves adjusting the optimization algorithm to consider the binding
affinities or other relevant properties for multiple targets at once. We
decide to accept this suggestions as building a well-trained machine learning
models to predict the binding affinities between inhibitors against cocaine
addiction and targets (such as DAT, NET, and SERT) is feasible. We have
collected such inhibitor data from the ChEMEL database [16] in our previous
work [17] to build multi-target models. Additionally, the prospect of
adjusting the optimization algorithm piqued our interest, and we plan to
solicit more detailed suggestions from GPT-4 in 6.

The second suggestion from GPT-4 proposes the development of specialized
encoders and decoders to address the challenges associated with multiple
targets. We elected to forgo this suggestion since we are not inclined to re-
train our existing GNC encoder and decoder. Concurrently, the third suggestion
from GPT-4 advocates for the development of novel screening methods to
evaluate the effectiveness of a molecule against multiple targets. We also
dismissed this recommendation since numerous approaches are already equipped
to tackle this challenge [11, 12, 13, 18] and the scope of our study does not
focus on the development of screening methods.

Moreover, GPT-4 suggested considering adjustments to the similarity constraint
to accommodate multiple reference molecules. We deemed this perspective
particularly insightful and will delve into it in Section 2.3.1. Finally,
GPT-4 recommended integrating the GNC model with other techniques specifically
tailored for multi-target tasks. For instance, GPT-4 proposed the
incorporation of alternative machine learning methodologies to predict the
effectiveness of a molecule against multiple targets, which would then guide
the generation of new molecules within the GNC framework. While this
recommendation appeared somewhat vague, we sought more detailed explanations
in Section 2.3.3.

List of dialogues 5 Suggestions of ChatGPT regarding the ”multi-target”
purpose.

After assessing the recommendations from the 1st persona of GPT-4, we were
specifically intrigued by its first suggestion concerning adjustments to the
optimization algorithm. Given that our previous optimization process in the
GNC model was conducted through gradient descent in the latent space, we
solicited insights from GPT-4 on potential mathematical or statistical
approaches that could be employed to enhance this optimization process within
the latent space. Consequently, GPT-4 provided us with five potential
strategies, including: 1) Multi-Objective Optimization, 2) Regularization
Techniques, 3) Stochastic Optimization, 4) Bayesian Optimization, and 5)
Reinforcement Learning. Among these, stochastic optimization piqued our
interest as strategies involving stochastic-related algorithms have gained
popularity in the diffusion models, which have achieved remarkable success in
generative tasks. In light of this, we would like to delve deeper into
stochastic-related approaches to tap its potential in generating promising new
molecules with multi-target specificity, especially in advancing our research
in anti-cocaine addiction drug discovery.

List of dialogues 6 Suggestions of ChatGPT regarding the optimization process.

Therefore, our follow-up question to GPT-4 pertained to the application of
stochastic-based methods, particularly those employed in diffusion models
[19], to the optimization process involved in latent space editing within our
GNC model. The 7 shows the feedback from GPT-4. First, GPT-4 provided a
succinct idea of the diffusion model, which elucidated that this model
introduce stochastic noise into data through a series of diffusion steps that
guided by a neural network, which is trained to reverse the diffusion process
to reconstruct desired data samples from the noise. This explanation aligns
well with existing comprehension of diffusion models. Next, GPT-4 advised
applying an approach similar to that used in diffusion models to guide the
optimization process within the latent space of our GNC model. Instead of
employing the conventional gradient descent, GPT-4 recommended the integration
of stochastic updates for enhanced manipulation of our latent space vectors.
As highlighted by GPT-4, this approach has several benefits: 1) avoidance of
local minima issue, which is often a challenge in optimization tasks, 2) a
balance between exploration and exploitation through noise, which is
imperative for the generation of multi-target inhibitors, and 3) the
capability to generate more diverse and natural molecules with the noise
introduced.

List of dialogues 7 Suggestions of ChatGPT regarding the stochastic
optimization.

We decided to partially accept the suggestions from GPT-4, given that our
previous work had already incorporated a perturbation of the encoded latent
vector using standard Gaussian noise to aid in the generation of novel
compounds [15]. This regulatory scheme is referred to as the Latent Space
Randomization (LSR) output. Although LSR can help generate new compounds that
significantly diverge from the initial seed (note: the term ’seed’ refers to
the initial point of origin or reference from which further variations or
iterations are developed), it compromises the faithfulness of the decoder.
This is because the LSR vector from the generator deviates from the original
distribution that the well-trained decoder is accustomed to. Therefore, in
this work, rather than merely adding Gaussian noise to the latent space
vector, we aim to seek deeper and more detailed insights from GPT-4 regarding
how to implement stochastic-related approaches to guide the optimization
process within the latent space. Our intention is to maintain the faithfulness
of the decoder while also promoting diversity in the generation of novel,
multi-target inhibitors.

Seeking further insights from GPT-4 on how we might implement stochastic-
related approaches to guide the optimization process in the latent space, we
received an initially vague response. GPT-4 suggested that we need to define a
stochastic process to direct the optimization in the latent space. However,
this response lacked the specificity and utility we needed. Thus, we posed a
follow-up question, seeking more clarity on the specific stochastic
differential equations (SDEs) that could be employed in our GNC model. With
more specific request presented to GPT-4, it suggested us to apply Langevin
equation to our GNC model. The Langevin equation describes the dynamics of
diffusion processes, such as the random motion of particles over time in the
particle’s velocity space. This equation takes into account both deterministic
forces and random forces. We decided to proceed with this suggestion, as in
our context, we can treat the force that pushes the system towards lower
energy as the deterministic force, while the random force in the Langevin
equation can be considered as the force prompting the system to explore the
latent space. With an initial seed (i.e., the initial latent space vector)
given to our molecular generator, we can iteratively update it according to
the Langevin equation. This process could potentially lead to the creation of
a new and optimized molecule. We detail the development of this Langevin
dynamic inspired optimization method in the following section.

####  2.2.4 Methodology clarification: ChatGPT’s explanatory function

We also give our GPT-4 a second persona as a professional researcher who is
well-versed in diffusion models and statistical methodologies. This persona
will take the role of methodology clarification and explanatory, which would
guide us in understanding complex mathematics and statistical approaches.
Notably, this persona has been instrumental in helping us understand the
concepts such as Langevin equation, Fokker-Planck equation, Itô’s lemma,
Wiener process, and Gaussian white noise [20].

Despite the significant contributions of this second persona in understanding
a range of theoretical concepts, it provided inaccurate definitions of the
Fokker-Planck equation and Langevin equation on certain occasions. We had to
correct the model and prompt it repeatedly until it produced the accurate
definitions. Importantly, we wish to emphasize that this persona of GPT-4
primarily serves as a source of explanations and references. Researchers
always bear the responsibility to ensure the reliability of responses from
GPT-4 through meticulous cross-validation of the provided information. Details
about the dialogue with 2nd persona of GPT-4 can be found in the Supporting
Information.

####  2.2.5 Coding efficiency: Utilizing ChatGPT’s coding ability

Our third persona assignment to GPT-4 is as an expert Python coder,
specifically knowledgeable in artificial intelligence and figure generation
using tools such as Plotly, a popular data visualization library. This persona
is intended to provide coding assistance, including debugging, generating
figures, and offering insightful feedback based on error messages. This is to
aid researchers in enhancing their coding efficiency.

Furthermore, we integrated GitHub Copilot into our VS Code development
environment. GitHub Copilot, a product developed collaboratively by GitHub,
OpenAI, and Microsoft, provides autocomplete-style suggestions to expedite the
coding process. It employs a generative AI model capable of understanding code
context and generating appropriate code snippets, thereby significantly aiding
in coding tasks and offering a smooth coding experience. Details about the
dialogue with 3rd persona of GPT-4 can be found in the Supporting Information.

###  2.3 ChatGPT assisted strategization of anti-cocaine addiction drug
discovery: Key interventions and results

####  2.3.1 ChatGPT guided strategy for selection of references and seed
molecules

Choosing suitable reference compounds is crucial as they guide the SGNC in
generating novel molecules effective against multiple cocaine transporters.
The 1st persona of ChatGPT suggested us consider modifications to the
similarity constraints (refer to 5 suggestion 4). Pursuing further clarity, we
asked GPT-4 about what similarity score that we can use. In response, we were
provided with five distinct metrics, including 1) Tanimoto similarity, 2)
cosine similarity, 3) dice similarity, 4) euclidean distance, 5) molecular
shape similarity as indicated in 8.

List of dialogues 8 Suggestions of ChatGPT regarding the the similarity
scores.

After limiting our molecule representations to latent space vectors, GPT-4
pinpointed cosine similarity as the most suitable metric. The reasons are
given in the 9. After checking multiple references [21, 22], we found that
cosine similarity $S_{\text{C}}$ is widely used in measuring similarities
between molecules. Therefore, we decided to proceed with the suggestion from
GPT-4. The mathematical definition of cosine similarity $S_{\text{C}}$ can be
found in the Supporting Information S1.2.

List of dialogues 9 Reasons of why choosing the cosine similarity as the
metric for similarity score.

In addition to similarity scores, we consulted with GPT-4 regarding additional
factors to consider when selecting reference molecules. GPT-4 highlighted four
critical parameters: binding affinity, pharmacokinetics, molecular weight, log
$P$, and number of rotatable bonds of each reference molecule (refer to 10).
Given that our focus is on choosing candidate reference compounds rather than
optimizing leads, we elected not to factor in the pharmacokinetic properties.
Furthermore, since the number of rotatable bonds correlates with binding
affinity, will only take binding affinities into consideration. Besides, as
suggested in 12, the selection of reference compounds will also follow the
Lipinski’s rule of five [23].

List of dialogues 10 Consideration of important factors in selecting reference
compounds.

Therefore, guided by the GPT-4, we decided to select one reference compound
from each of the DAT-Inhibitors, NET-Inhibitors, and SERT-Inhibitors datasets
(detailed information of datasets can be found in Section 4.1). They are
CHEMBL113621 from DAT-Inhibitors, CHEMBL1275709 from NET-Inhibitors, and
CHEMBL173344 from SERT-Inhibitors. Each reference molecule has binding
affinity to its respective transporter less than -9.54 kcal/mol. To be noted
that a $\Delta G$ value less than -9.54 kcal/mol (or $K_{\rm i}$ less than 0.1
$\mu$M) indicates that the drug binds very tightly to its target [24].
Moreover, the selection of reference compounds follow the Lipinski’s rule of
five, which stipulates an orally active drug should meet four physicochemical
criteria: 1) molecular weight (MW) $\leq 500$ daltons, 2) octanol-water
partition coefficient (log $P$) $\leq 5$, 3) the number of hydrogen bond
donors (nHD) $\leq 5$, 4) the number of hydrogen bond acceptors (nHA) $\leq
10$. Furthermore, each reference molecule displayed an average cosine
similarity (Avg $S_{\text{C}}$) greater than 0.40 to its respective dataset.
Notably, within the DAT-Inhibitors dataset, 31 molecules showed a similarity
score exceeding 0.7 for their selected reference molecules. Similarly, 15
compounds in the NET-Inhibitors and 12 in the SERT-Inhibitors also achieved
scores above 0.7 with their chosen reference molecules. A summary of
physicochemical properties of three reference compounds can be found in Table
1 and their 2D molecular structures can be viewed in Figure 2 a), b), and c).

For the seed compound, we selected a molecule with predicted binding
affinities of -7.44, 13.36, and -13.13 kcal/mol for DAT, NET, and SERT,
respectively. Despite its weak inhibitory effect on DAT, we adjusted the
hyperparameters in the stochastic molecular generator to enable the newly
generated compounds to share more moieties with DAT inhibitors, thereby
compensating for the deficiency of this week binding to DAT.

Table 1: Summary of three reference molecules target to DAT, NET, and SERT, respectively. The molecular weight (MW), log of octanol-water partition coefficient (log $P$), the number of hydrogen bond donors (nHD), and the number of hydrogen bond acceptors (nHA) of each reference molecule satisfy the Lipinski’s rule of five. The binding affinity ($\Delta G$) corresponds to each transporter are all less than -9.54 kcal/mol. The average cosine similarity (Avg $S_{\text{C}}$) of each reference molecules are all greater than 0.40.  ChEMBL ID | Transporter | MW (dalton) | log $P$ | nHD | nHA |  $\Delta G$ (kcal/mol) | Avg $S_{\text{C}}$
---|---|---|---|---|---|---|---
CHEMBL113621 | DAT | 300.140 | 4.464 | 0 | 2 | -14.18 | 0.45
CHEMBL1275709 | NET | 283.190 | 3.153 | 1 | 2 | -13.77 | 0.43
CHEMBL173344 | SERT | 253.160 | 3.174 | 1 | 3 | -13.58 | 0.40

####  2.3.2  ChatGPT aided multi-objective drug-target interaction modeling

To predict the binding affinities of newly generated molecules to four targets
(DAT, NET, SERT, and hERG), we aimed to construct four binding affinity
predictors. Initially, we sought guidance from GPT-4’s 3rd persona on the most
suitable machine learning algorithms, given our dataset’s specific attributes
(sample size, feature size, and label). The recommendations of GPT-4 are
detailed in 11. In our former study [10], gradient boosting decision trees
(GBDT) were utilized to train binding affinity predictors on DAT-Inhibitors,
NET-Inhibitors, SERT-Inhibitors, and hERG-Inhibitors. The resulting 10-fold
Pearson correlation coefficients were 0.78, 0.76, 0.76, and 0.68 respectively,
serving as our baseline. Given our access to robust computational resources
via high-performance computers (HPC), we decided to develop four deep neural
networks. All four predictors were built and trained using PyTorch. Each
network consisted of three hidden layers, with 512, 1024, and 512 hidden
neurons respectively. The networks were trained over 1000 epochs, with a
learning rate of 0.0001 for the first 500 epochs and 0.00001 for the remaining
500 epochs. Additionally, The Adam optimizer was chosen for this task.
Researchers can inquire template of PyTorch code to build a deep neural
network via ChatGPT (see Supporting Information S4.3). Moreover, as suggested
by GPT-4, to get a more robust estimate of the model performance, we also
evaluate the Pearson correlation coefficient (R) and root-mean-square error
(RMSE) of 10-fold cross validation of four predictors, which are reported in
the Table 2.

List of dialogues 11 Consideration of machine learning algorithms that can be applied to build binding affinity predictors. Table 2: Dataset summary. Four datasets are utilized, each containing SMILES strings of inhibitors targeting DAT, NET, SERT, and hERG respectively. Alongside each SMILES string, the respective binding affinity in the unit of kcal/mol is also included as the label for each sample. Additionally, the final two columns represent the 10-fold cross validation Pearson correlation coefficient (R) and root-mean-square rrror (RMSE) for each binding affinity predictor across the four datasets.  Dataset name | Sample size | Binding affinity range (kcal/mol) | 10-fold R | 10-fold RMSE
---|---|---|---|---
DAT-Inhibitors | 2662 | [-14.18, -2.90] | 0.8212 | 0.8979
NET-Inhibitors | 2981 | [-14.63, -5.47] | 0.7732 | 0.9683
SERT-Inhibitors | 4341 | [-15.00, -5.64] | 0.8022 | 0.9448
hERG-Inhibitors | 6298 | [-13.84, -3.27] | 0.8092 | 0.7981

Figure 2 d) and e) shows the experimental and predicted binding affinity
distribution on the four training sets: DAT, NET, SERT, and hERG. The
distribution of predicted binding affinities align well with the experimental
values, which shows that our binding affinity predictor is reliable. The grey
region signifies the zone where the binding affinity is less than -9.54
kcal/mol (i.e $K_{\rm i}=0.1\mu$M), generally considered as the cut-off for
recognizing active compounds. The pink region highlights the zone where the
binding affinity is more than -8.18 kcal/mol (i.e $K_{\rm i}=1\mu$M), a
criterion set to prevent hERG-related side effects. We generated around 16
million novel compounds from stochastic-based molecular generator, and their
distribution of predicted binding affinities are depicted in Figure 2 f). It
is worth mentioning that the predicted binding affinity of newly generated
molecules, targeted to DAT, NET, and SERT, all fall below -9.54 kcal/mol. This
results from the properly adjusted hyperparameters and threshold of step size
in the stochastic-based molecular generator, aimed at producing more active
compounds. However, as hERG inhibitors were not included as reference
compounds in the generation of new molecules, only about half of the generated
compounds meet the criteria set to prevent hERG-related side effects.

Figure 2: a) 2D molecular structures of reference compound with ChEMBL ID a)
CHEMBL113621 from DAT-Inhibitors dataset, b) CHEMBL1275709 from NET-Inhibitors
dataset, and c) CHEMBL173344 from SERT-Inhibitors dataset. 2D molecular
structures are rendered by an online software SmilesDrawer 2.0 [25]. d)
Distribution of experimental binding affinities for the four training datasets
(DAT-Inhibitors, NET-Inhibitors, SERT-Inhibitors , and hERG-Inhibitors). e)
Distribution of predicted binding affinities derived from the four deep neural
network predictors. f) Distribution of predicted binding affinities for newly
generated inhibitors targeting DAT, NET, SERT, and hERG. g) Screening of 330
preliminary multi-target drug candidates. The color of each point represents
the predicted binding affinities to DAT (purple, g)), NET (green, h)), and
SERT (blue, i)). The light purple, green, and blue frames outline the medium
ranges of 10 ADMET, physicochemical, and medicinal chemistry properties,
respectively, while the dark purple, dark green, and dark blue frames outline
the excellent ranges of these properties.

####  2.3.3 ChatGPT assisted virtual screening of multi-target drug candidates

By editing the latent space vector of Seq2Seq AE, a total of 16 billion
vectors are generated from our By editing the latent space vector of the
Seq2Seq AutoEncoder (AE), we were able to generate a vast number of vectors
(around 16 billion) using our stochastic-based molecular generator. These
vectors are then decoded into molecules through the GRU Decoder of the Seq2Seq
AE. Next, we proceeded by implementing a filtering process, during which we
removed any duplicated molecules and predicted their corresponding binding
affinities to four target proteins: DAT, NET, SERT, and hERG. Any generated
molecules meeting the binding affinity requirement (i.e., $\Delta G<$ -9.54
kcal/mol for DAT, NET, SERT and $\Delta G>$ -8.18 kcal/mol for hERG) were
considered preliminary multi-target drug candidates. A total of 330
preliminary drug candidates pass the filtering test. Moreover, the
similarities between 330 preliminary drug candidates and three references
compounds are all less than 0.5, indicating the the high novelties of
generated multi-target molecules.

Next, we sought advice from GPT-4’s 1st persona on criteria for selecting
drug-like lead compounds. Due to paper length constraints, a concise version
of responses can be found in 12. Acting on these suggestions, we utilized in
silico tools to predict the Absorption, Distribution, Metabolism, Excretion,
and Toxicity (ADMET) properties of each candidate molecule. Specifically, we
examined 10 properties of 330 preliminary multi-target drug candidates through
ADMETlab 2.0. This platform aims to provide systematic evaluation of ADMET
properties, physicochemical properties, and an assessment of medicinal
chemistry friendliness.

The 10 properties assessed in this work included: Caco-2 (the human colon
adenocarcinoma cell lines) permeability, $\text{F}_{20\%}$ (the human oral
bioavailability 20%), Pgp-substrate (the substrate of P-glycoprotein), Pgp-
inhibitor (the inhibitor of P-glycoprotein), VD (volumn density),
$\text{T}_{1/2}$ (The half-life of a drug), FDAMDD (The maximum recommended
daily dose), SAS (synthetic accessibility score), log $P$ (the logarithm of
the n-octanol/water distribution coefficient), and log $S$ (the logarithm of
aqueous solubility value). The optimal range of 10 properties can be found in
Table 3.

List of dialogues 12 Silico analyses to assess the drug-likeness of candidate
molecules suggested by ChatGPT.

Figure 2 g), h), and i) depict the screening results on 330 preliminary multi-
target drug candidates. The color gradient in each panel signifies the
predicted binding affinities of the molecules to their respective targets.
Specifically, in the Figure 2 g), the color of each point indicates the
binding affinities to DAT. Similarly, in the Figure 2 h) and i), the colors of
points represent the binding affinities to NET and SERT, respectively. It can
be seen that the binding affinity of drug candidates for SERT is stronger than
that for DAT and NET. The frames outline the medium (light purple, green, and
blue) and excellent ranges (dark purple, green, and blue) for the 10 evaluated
ADMET, physicochemical, and medicinal chemistry properties. Researchers can
access the code of scatter plot in python via ChatGPT swiftly Supporting
Information S4.3.

Figure 2 g), h), and i) indicate that all the drug candidates have favorable
volume density (VD) and synthetic accessibility score (SAS) values. However,
only a select few demonstrate preferable FDAMMDD, $\text{F}_{20\%}$,
$\text{T}_{1/2}$, Pgp-sub, and Pgp-inh. Among all, 15 candidate drugs fall
within the medium range for all properties, thus are deemed potential multi-
target anti-cocaine lead compounds. We evaluated the SMILES strings of 15
potential anti-cocaine addiction lead compounds that could target multiple
transporters: DAT, NET, and SERT. Notably, all 15 lead compounds satify
Lipinski’s rule of five. As suggested by ChatGPT, the molecular docking
analysis of 15 lead compounds can be found in Section 2.3.5.

Noted, in Section 2.2.3, GPT-4 introduced a generalized suggestion about
leveraging alternative machine learning approaches to predict effectiveness of
a given molecule against various targets. A detailed discussion of potential
ML methods is provided in 13. In fact, ADMETlab2.0 serves as an exemplary
application of these alternative methodologies. Specifically, it incorporates
both Graph Neural Networks (GNNs) and multitask learning, leading to enhanced
performance across many modeled endpoints.

List of dialogues 13 Alternative machine learning approaches to predict
effectiveness of a given molecule against various targets.

####  2.3.4 ChatGPT assisted identification of potential multi-target leads

Figure 3 a) represents the SMILES strings of the 15 candidate drugs, detailing
whether each drug falls within the excellent or medium range for the evaluated
10 properties. Green pixels denote a candidate drug that meets the excellent
range for each of the 10 evaluated properties, while white pixels indicate a
drug that only achieves the medium range. The color gradient signifies the
percentage of properties within the excellent range for each compound. The
$y$-axis displays the SMILES strings of the 15 candidate drugs along with
their corresponding IDs. Notably, Drug 15 is the sole candidate that exhibits
an excellent FDAMDD value, though its $\text{T}_{1/2}$ is relegated to the
medium range. Furthermore, the drugs with IDs 4, 7, 10, and 15 demonstrate a
relatively high percentage of properties within the excellent range.

In addition, we aimed to analyze the moieties of 15 potential multi-target
anti-cocaine addiction lead compounds. Figure 3 b) depicts the 2D molecular
structures of three reference compounds (CHEMBL113621, CHEMBL1275709, and
CHEMBL173344) and 15 potential anti-cocaine lead compounds. In this figure,
purple, green, and blue spots depict the CHEMBL113621-like,
CHEMBL1275709-like, and CHEMBL173344-like moieties, respectively.
Additionally, red spots highlight novel moieties that are not present in the
three reference compounds. Leads 1, 2, 3, 8, 9, 13, 14, and 15 share more
CHEMBL113621-like and CHEMBL173344-like moieties, while Leads 4, 5, 6, 7, 10,
11, and 12 sahre more CHEMBL1275709-like moieties. For a systematic
examination of the functional groups within these lead compounds, we leaned on
guidance from ChatGPT, which simulated the role of a chemist skilled in
interpreting SMILES (Simplified Molecular Input Line Entry System) strings. An
illustrative example of this guidance can be seen in 14. A comprehensive
analysis is available in the Supporting Information S4.4. Assisted by ChatGPT,
we successfully undertook the analysis of the 2D molecular structures of the
15 lead compounds.

List of dialogues 14 ChatGPT acts as a chemist to guide the analyze of
functional groups. Cpmplete interactions with ChatGPT can be found in
Supporting Information S4.4.

Lead 1 features a benzene ring linked to a bicyclic structure and a
dimethylamine branch. It is worth mentioning that molecules with a nitrogen-
containing ring (such as piperidine) attached to a benzene ring are frequently
found in bioactive compounds. Lead 2 comprises a benzene ring attached to a
modified piperazine ring, which is further connected with a cyclopentane
group.. This type of structure is prevalent in numerous bioactive molecules,
including some pharmaceutical drugs. Lead 3 contains a chlorobenzene ring
coupled with a substituted piperazine ring. Besides, there is a methylamine
group attached to the benzene ring. This structure might have potential
psychoactivity, as structures featuring a nitrogen-containing ring connected
to a benzene ring are commonly observed in many psychoactive compounds such as
Phenethylamines, Tryptamines, and Ergolines. Lead 4 encompasses a benzene ring
with an attached dimethylamine group. In addition, the benzene ring is linked
to a bicyclic structure that includes a piperidine ring and an aldehyde group.
This molecule could potentially be bioactive due to the presence of both a
benzene ring and a nitrogen-containing ring. Lead 5 shares very similar
structure as Lead 4. The only difference is that the bicyclic structure of
Lead 5 includes propionaldehyde group instead of aldehyde group.

Leads 6, 10, 11, and 12 all feature a benzene ring connected to a
dimethylamine group and a bicyclic structure. Lead 7 consists of a benzene
ring linked to a substituted alkene group, along with a bicyclic structure
that includes both a pyrrolidine ring and a piperazine ring. Additionally,
this bicyclic structure is connected by a propionadehyde group. Lead 8
includes a benzene ring with an attached dimethylamine group, connected to a
bicyclic structure that incorporates a piperidine ring and an additional
pyrrolidine ring. Lead 9 comprises a benzene ring linked to an alkyne group,
and a complex structure with a piperidine ring and a three-membered nitrogen-
sulfur ring. Notably, molecules with sulfur-containing rings, such as
penicillin and angiotensin-converting enzyme (ACE) inhibitors, are recognized
as bioactive. Lead 13 incorporates a benzene ring with an attached
dimethylamine group, connected to a bicyclic structure that includes
pyrrolidine ring and a cyclohexane ring which is also connected by a formyl
group. Lead 14 is composed of a chloroethane group linked to two pyrrolidine
rings and a benzene ring. Finally, Lead 15 consists of a benzene ring linked
to a dimethylamine group via an alkene group and attached to a bicyclic
structures composed by two pyrrolidine rings.

Figure 3: a) The SMILE string of 15 potential anti-cocaine lead compounds that
could target to multiple transporters DAT, NET, and SERT. Green pixels
indicate a given candidate falls within the excellent range for each of the 10
evaluated ADMET, physicochemical, and medicinal chemistry properties, while
the blue pixels describe a give candidate drug only falls within the medium
range of these properties. The color gradient represents the percentage of
properties within the excellent range for each given compound. b) Illustration
of the 2D molecular structures of three reference compounds and 15 potential
anti-cocaine lead compounds, which may target multiple transporters (DAT, NET,
and SERT). Purple, green, and blue spots represent the CHEMBL113621-like,
CHEMBL1275709-like, and CHEMBL173344-like moieties, respectively. Red spots
highlight novel moieties that are not present in the three reference
compounds. All 2D molecular structures are rendered by an online software
SmilesDrawer 2.0 [25].

####  2.3.5 ChatGPT assisted analysis of cocaine transporter and inhibitor
interactions

As mentioned in 12, ChatGPT suggested to perform molecular docking to predict
how each molecule binds to the target. We decided to accept this suggestion as
the understanding of the molecular mechanism of drug-target interactions is
vital in identifying effective drug candidates. We also seek the expertise
from ChatGPT for the installation guidance of AutoDock Vina [26] and guidance
to execute molecular docking procedures (see 15) between 15 lead compounds and
target proteins DAT (PDB ID: 4XPA) and SERT (PDB ID: 6DZZ). To be noted, due
to the lack of NET structures in the Protein Data Bank, we do not included
molecular interaction analysis of candidate leads with NET. Moreover, we want
to visualize 2D protein-ligand interaction diagrams, as they offer a
streamlined representation of protein-ligand interactions, highlighting
crucial residues, hydrogen bonds, and more. ChatGPT recommended several
popular software tools for this purpose, including LigPlot+ and Maestro. In
this work, we chose LigPlot+ for our visualization needs.

List of dialogues 15 ChatGPT assists in the software installation and
application. Complete dialogue is in the Supporting Information S4.3.

Our observations highlight the critical role of hydrogen bonds in the
molecular interactions. The interactions of the drug candidate with DAT and
SERT feature two and one hydrogen bonds, respectively, thereby contributing to
the high potency of the molecule on the transporters. The first and third
columns in Figure 4 illustrate the docking of Lead 15 and its molecular
interactions with DAT and SERT. We have identified 15 nearly optimal leads. As
demonstrated in the second and fourth columns of Figure 4 a), Lead 4
establishes two hydrogen bonds with DAT and four hydrogen bonds with SERT. Of
the two bonds with DAT, one is formed between an oxygen atom on the residue
Gln209(A) and a nitrogen atom on the compound, and the other involves an
oxygen atom in a hydroxyl group of the compound interacting with a nitrogen
atom on residue Asn207(A) of DAT. Among the four hydrogen bonds formed between
Lead 4 and SERT, two involve the same oxygen atom interacting with a nitrogen
atom on residues Leu99(A) and Tyr176(A) of SERT, while the other two bonds are
formed by nitrogen atoms on Lead 4, interacting with oxygen atoms on residues
Ser438(A) and another unidentified residue of SERT. Figure 4 b) depicts a
hydrogen bond in the molecular interactions between candidate Lead 9 and SERT.
This bond is formed by a nitrogen atom on the compound and an oxygen atom in a
hydroxyl group on residue Phe335(A) of SERT. However, no hydrogen bond is
observed in its interactions with DAT. This suggests that other types of
interactions, such as hydrophobic bonds, may play a major role in the high
binding affinity between Lead 9 and DAT.

The molecular docking poses of Lead 13 on DAT and SERT are illustrated in the
1st and 3rd columns of Figure 4 c). In the second column of Figure 4 c), a
single hydrogen bond can be observed between a nitrogen atom of Lead 13 and an
oxygen atom on the residue Glu161(A) of DAT. Conversely, no hydrogen bond is
detected between Lead 13 and SERT, as demonstrated in the 4th column of Figure
4 c). The molecular docking poses of Lead 15 on DAT and SERT are portrayed in
Figure 4 d), presenting the compound’s docking positions at the centers of
both transporters. In its interaction with DAT, Lead 15 forms two hydrogen
bonds through a nitrogen atom in a five-membered nitrogen heterocycle. This
nitrogen atom interacts with oxygen atoms in two hydroxyl groups, which are
attached to the residues Asp475(A) and Tyr123(A) of DAT. Moreover, a hydrogen
bond exists between the candidate drug Lead 15 and SERT. This bond is formed
by the same nitrogen atom in the five-membered nitrogen heterocycle, which
interacts with an oxygen atom in a hydroxyl group attached to the residue
Ala169(A) of SERT. The molecular interaction with other 11 candidate leads can
be found in the Supporting Information S3.

Figure 4: Predicted docking poses of selected lead candidates to DAT (1st
column) and SERT (3rd column) by AutoDock Vina. DAT is colored in purple, and
SERT is presented in blue. The 2nd and 4th columns demonstrate the molecular
interaction of these leads with DAT and SERT, respectively. The final column
portrays the physicochemical properties of the lead candidates, which include
MW (molecular weight), log P (logarithm of octanol/water partition
coefficient), log S (logarithm of the aqueous solubility), log D (log P at
physiological pH 7.4), nHA (number of hydrogen bond acceptors), nHD (number of
hydrogen bond donors), TPSA (topological polar surface area), nRot (number of
rotatable bonds), nRing (number of rings), MaxRing (number of atoms in the
largest ring), nHet (number of heteroatoms), fChar (formal charge), and nRig
(number of rigid bonds). Here the purple dots denote the minimal value and the
blue dots indicate the maximal value within the optimal range. The red lines
represent the values of the properties for each lead candidate. The figures
are categorized as follows: a) candidate lead 3, b) candidate lead 9, c)
candidate lead 13, and d) Candidate lead 15.

##  3 Discussion

###  3.1 Scrutinizing chatbots

While chatbots are powerful large language models, they are not infallible.
Their predictions are heavily reliant on the training data, which may lead to
incomplete, outdated, bias, or skewed understandings of certain contexts.
Consequently, this could result in the generation of misleading narratives and
incorrect information. Therefore, it is essential for researchers to employ
chatbots with appropriate care and vigilance. Scientists should not solely
rely on chatbots for their research pursuits and should consistently cross-
check the information generated by chatbots. Notably, the role of a chatbot
like GPT-4 is to assist researchers, not to replace them. In our current
project, we have employed GPT-4 to assist in our anti-cocaine addiction drug
discovery process, as delineated in Figure 5 a).

We first will assign a proper persona to GPT-4 and then ask it with questions.
Once we get the response from GPT-4, it is crucial to decide whether to accept
the responses or not. If the information aligns well with the literature and
our expertise, we will accept the responses and proceed with the suggestions
of GPT-4. Otherwise, we will either reject the answer or seek further
clarification to GPT to get alternative feedback.

For example, when acting as a chemist to analyze the functional groups of
Leads 2, 7, 8, 9, 13, and 15, ChatGPT provided inaccurate information.
Specifically, for Lead 15, ChatGPT identified a structure where a benzene ring
is linked to a dimethylamine group via an alkene group and connected to a
piperazine ring. However,the dimethylamine group is connected to a piperazine
ring instead of a piperazine ring. This misinformation in the interaction with
ChatGPT is documented in 16. Thus, it is paramount for researchers to verify
the accuracy and reliability of responses from ChatGPT using their expertise.

List of dialogues 16 Misinformation provided by ChatGPT. Complete interactions
with ChatGPT can be found in Supporting Information S4.4.

Beside, we noticed that ChatGPT does not perform well in the role of
methodology explanation. The 2nd persona of ChatGPT frequently provide wrong
definitions. Then we will not accept the response from ChatGPT and seek
further clarification as suggested in Figure 5 a). For example, we can point
the mistakes out and refer useful information to ChatGPT to help it modify its
response as indicate in 17.

Additionally, we noticed that ChatGPT does not perform well when providing
methodological explanations. Its responses from the 2nd persona contain some
incorrect definitions and explanations. In such cases, we opted not to accept
responses from ChatGPT, and seek further clarification followed by the
workflow in Figure 5 a). An effective approach involved pointing out the
inaccuracies to ChatGPT and supplying it with accurate references or
information, prompting it to adjust its responses. Specific instances of these
methodological inaccuracies are detailed in 17. Despite the inaccurate
responses provided by the 2nd persona of ChatGPT, it remained invaluable in
helping us grasp numerous theoretical concepts and their interrelations,
serving as an effective browser tool.

List of dialogues 17 A succinct dialogue highlighting inaccuracies of ChatGPT
responses and suggestions for corrections. Complete interactions with ChatGPT
can be found in Supporting Information S4.2.

###  3.2 Autoencoder reconstruction rate of molecules

A Sequence-to-Sequence Autoencoder (Seq2Seq AE) is a specific type of neural
network model designed to learn a compressed representation of input data and
reconstruct this data from the obtained representation. The core objective of
such an autoencoder is to minimize the discrepancy between its input and
output data. In this study, we initially fed the Seq2Seq AE with SMILES
strings derived from four distinct datasets to examine their respective
reconstruction rates. The calculated reconstruction rates for DAT-Inhibitors,
NET-Inhibitors, SERT-Inhibitors, and hERG-Inhibitors datasets are 0.958,
0.970, 0.968, and 0.950 respectively. These values signify a successfully
implemented autoencoder model.

In addition to the aforementioned, we verified the reconstruction rate of
molecules generated via the Seq2Seq AE. After eliminating duplicated SMILES
strings from the generated set, the resulting reconstruction rate stood at
0.996. This high reconstruction rate implies that the distribution of our
generated molecules closely mirrors that of the original dataset processed by
Seq2Seq AE, underscoring the reliability of the molecules generated by our
method.

Figure 5: a) Flowchart of implementing ChatGPT as a virtual guide. b)
Distribution of latent space vectors across various datasets. The $x$-axis
represents the latent space index, which ranges from 0 to 511, while the
y-axis denotes the absolute average value of the latent space vector
corresponding to each index. The purple, green, and blue figures represent the
latent space vector distribution from the DAT-Inhibitors, NET-Inhibitors, and
SERT-Inhibitors respectively. c) Distribution of latent space vectors of
generated molecules. The brown panel illustrates the latent space vector
distribution from generated molecules from the fine-tuned stochastic-based
molecular generator. The yellow distribution portrays generated molecules that
have been processed by the GNC a second time. The grey distribution
corresponds to generated molecules from an untuned stochastic-based molecular
generator.

###  3.3 Patterns sensitive latent space vector distributions

Initially, we introduced random Gaussian noise, with a range from -1 to 1,
into the stochastic-based molecular generator. However, these perturbations in
the latent space vectors resulted in weird SMILE strings once decoded. Seeking
guidance, we consulted ChatGPT as referenced in 18, which provided us with
eight potential solutions. After reviewing these suggestions, we aligned with
the first and second suggestions that echoed findings from our previous work,
emphasizing that random perturbations in the latent space can destabilize the
Seq2Seq AE model [14]. To ensure the reliability and effectiveness of the
decoder in Seq2Seq AE model, it is essential to maintain a similar
distribution pattern between the original latent space vectors and those
derived from the stochastic-based molecular generator. Therefore, we take
efforts to tune the noise that added to the stochatic-based molecular
generator, to guarantee the modified/edited latent space vectors retain a
representation that the GNC model has learned to decode effectively.

Figure 5 b) and c) depict distribution of latent space vectors across various
datasets. Here, the $x$-axis represents the latent space index (ranges from 0
to 511), and the $y$-axis shows the absolute average value of the latent space
vector corresponding to each index. The representation of absolute average
value helps in visualizing the pattern and magnitude of latent space vectors
across a broad index range. The purple, green, and blue panels depict the
latent space vector distribution from the DAT-Inhibitors, NET-Inhibitors, and
SERT-Inhibitors respectively. The discrepancy can be observed in the grey
panel of Figure 5 c) (particularly in the red boxed area). None of the
molecules generated by this untuned stochastic-based molecular generator
passed either the binding affinity requirements or ADMET tests. Subsequently,
we adjusted the Gaussian noise in the stochastic-based molecular generator to
ensure the edited latent spaces (represented in brown) exhibited a similar
distribution to the original ones as shown in Figure 5 b). This controlled
noise (ranges from -0.1 to 0.1) proved beneficial, leading to the generation
of 15 promising leads capable of targeting DAT, NET, and SERT. Additionally,
enlightened by ChatGPT (suggestion 8 in 18), we also implemented a feedback
loop where the molecules generated by our fine-tuned stochastic molecular
generator is re-encoded into the latent space. It is worth noting that these
re-encoded latent space vectors maintained a similar distribution, suggesting
that the modifications made to the latent vectors are within the learned
parameters of the SGNC.

List of dialogues 18 Solutions provided by ChatGPT regarding the random
perturbation issue in the stochastic-based molecular generator.

##  4 Methods

###  4.1 Datasets preparation

Four pharmaceutical targets are key to treating cocaine addiction and drug
discovery: Dopamine Transporter (DAT), Norepinephrine Transporter (NET),
Serotonin Transporter (SERT), and Human Etherá-go-go-Related Gene (hERG). DAT
is responsible for dopamine reuptake from synapses back into neurons,
terminating neurotransmitter signaling. This causes dopamine accumulation in
synapses and inducing intense euphoria. Similarly, NET is inhibited by
cocaine, leading to elevated norepinephrine levels in synapses, contributing
to stimulant effects. Furthermore, the inhibition of cocaine and SERT
increases serotonin levels in the synapse, resulting in mood elevation,
anxiety, and paranoia. Thus, a compound that concurrently modulates DAT, NET,
and SERT activities could potentially treat cocaine addiction. Additionally,
blocking the hERG potassium ion channel can lead to potentially fatal cardiac
arrhythmias. Therefore, it is also critical to consider binding affinity
between hERG and new generated leads.

In this study, we collected SMILES strings and binding affinities of
inhibitors targeting DAT, NET, SERT, and hERG from the ChEMBL database. A
summary of the size and label of datasets can be found in Table 2. We
leveraged these four datasets in three ways: 1) training four separate binding
affinity predictors based on the DAT-Inhibitors, NET-Inhibitors, SERT-
Inhibitors, and hERG-Inhibitors datasets respectively, 2) selecting molecules
from the DAT-Inhibitors, NET-Inhibitors, and SERT-Inhibitors datasets as
reference and seed compounds for the generation of new drug-like molecules,
and 3) inputting the SMILES strings from all four datasets into a sequence-to-
sequence AutoEncoder model to validate their successful reconstruction rate.

To be noted, we use the binding affinity in terms of Gibbs free energy
($\Delta G$) with units of kcal/mol, rather than the inhibition constant
($K_{\rm i}$) or half maximal inhibitory concentration (IC50). The Gibbs free
energy of binding provides a direct measure of the favorability of the binding
process, which indicates a more favorable binding interaction that can be
interpret in the drug-target interactions. The conversion from $K_{\rm i}$ to
$\Delta G$ is [27]:

$\Delta G=-RT\text{ ln}K_{\rm i}\approx 1.3633\times\log_{10}K_{\rm i}\text{
(kcal/mol)},$

where $R$ is the gas constant (1.987 cal/mol·K) and $T$ (298.15 K) is the
absolute temperature. In addition, the IC50 can be approximated to $K_{\rm i}$
in the case of competitive and uncompetitive inhibition according to
Kalliokoski [28] under:

$K_{\rm i}=\text{IC}_{50}/2.$

###  4.2 Stochastic-based generative network complex (SGNC)

In this section, after thorough evaluation and incorporation of suggestions
from GPT-4, we introduce the stochastic-based generative network complex
(SGNC) as a novel mathematical-AI model, whcih is designed to generate novel
molecules that potentially serve as effective treatments for cocaine
addiction. Specifically, these molecules are intended to target multiple sites
such as the Dopamine Transporter (DAT), Norepinephrine Transporter (NET), and
Serotonin Transporter (SERT).

Figure 1 illustrates the workflow of SGNC, which essentially consists of four
main components: 1) Sequence-to-Sequence AutoEncoder (shown in green), 2)
Binding Affinity Predictors (shown in yellow), 3) Stochastic-based Molecular
Generator (shown in blue), and 4) Analysis via ADMET Lab (shown in purple).
The dark arrows represent the training process, the brown arrows show the
validation process, and the red arrows indicate the generation process.

For the training process, we leveraged a well-established translation model,
specifically a sequence-to-sequence (Seq2Seq) AutoEncoder (AE). This model was
developed to map the International Union of Pure and Applied Chemistry (IUPAC)
representation of a molecule to its Simplified Molecular Input Line Entry
System (SMILES) representation, as mentioned in [29]. In our prior research,
we modified this model by switching the input from the IUPAC representation of
molecules to their corresponding SMILES strings.

The generation process involves the following main steps:

  1. 1.

We initially selected one molecule each from the DAT, NET, and SERT datasets.
These molecules were chosen because of their relatively high similarity within
the three datasets, thereby acting as our reference compounds. In addition, we
selected a compound known for its potency against all three targets to serve
as our seed compound.

  2. 2.

We then input the reference and seed compounds into the pretrained encoder and
extracted the corresponding latent vectors from the latent space of the
Seq2Seq AE. Subsequently, we modified the seed vector in the stochastic-based
molecular generator, using the information from the reference molecules as a
guide. As a result, the generator was capable of producing a large number of
new latent vectors. These vectors were then decoded into SMILES strings, which
are potentially effective against multiple targets, namely DAT, NET, and SERT.

  3. 3.

Furthermore, we input these decoded SMILES strings into our binding affinity
(BA) predictors to filter out molecules that meet our BA requirement (i.e.,
$\Delta G<-9.54$ kcal/mol on DAT, NET, SERT and $\Delta G>-8.18$ kcal/mol on
hERG).

  4. 4.

Finally, we used ADMETlab 2.0 to select drugable molecules from the generated
SMILES with desirable BA properties. This final step in the generation process
ensures that the compounds not only bind effectively to the desired targets
but also have the necessary absorption, distribution, metabolism, excretion,
and toxicity (ADMET) properties for a potential lead compound.

During the validation phase, we first input the SMILES strings from the DAT,
NET, SERT, and hERG datasets into our well-trained Seq2Seq AE model to obtain
decoded SMILES. Successful reconstruction of the input SMILES signifies the
reliability of the Seq2Seq AE model. Furthermore, we input the generated
SMILES, which have been processed through the stochastic-based molecular
generator, into the pre-trained Seq2Seq AE model. In case of unsuccessful
reconstruction, we adjust the hyperparameters of the stochastic-based
molecular generator until a high reconstruction rate is achieved. This process
indicates that the latent vectors edited by the stochastic-based molecular
generator maintain a similar distribution to the original latent space vectors
from the encoder, which further ensures that our SGNC model is capable of
generating chemically feasible compounds, reflecting its potential in drug
discovery applications.

####  4.2.1 Sequence-to-sequence autoencoder

The Sequence-to-sequence autoencoder (Seq2Seq AE) is an artificial neural
network model used for translating the IUPAC representation of a given
molecule into its SMILES string representation [29]. In our study, the Seq2Seq
AE accepts the SMILES representation of a molecule as the input for the
encoder. Subsequently, the latent space of the Seq2Seq AE preserves the
structural and functional properties of the provided SMILES. This low-
dimensional latent space representation can then be processed by the decoder
of the Seq2Seq AE to reconstruct the original SMILES representation. Here, the
network that used in encoder and decoder is the gated recurrent unit (GRU). In
this work, the pretrained Seq2Seq AE model was utilized from a previous work
by Winter et al [29].

The Seq2Seq AE model employed in our study was pretrained on 72 million
compounds [29] sourced from the ZINC15 and PubChem databases. All duplicate
entries within these databases were eliminated and subjected to RDKit [30]
filtering using the following criteria: 1) only organic molecules, 2)
molecular weight between 12 and 600 daltons, 3) more than 3 heavy atoms, 4)
partition coefficient log $P$ between -5 and 5, 5) sterochemistry was removed,
6) salts were stripped.

####  4.2.2 Binding affinity predictors

We constructed four binding affinity predictors based on four training
datasets: DAT-Inhibitors, NET-Inhibitors, SERT-Inhibitors, and hERG-
Inhibitors. These predictors are designed to estimate the binding affinity of
potential molecules to four critical targets: DAT, NET, SERT, and hERG. The
construction of the predictors involved the following steps:

  1. 1)

Feature extraction: molecular features (or fingerprints), were derived from
the latent space of a sequence-to-sequence AutoEncoder (Seq2Seq AE).

  2. 2)

Label assignment: The labels used for model training were the binding
affinities of the molecules to their respective targets.

  3. 3)

Model training: we trained the predictors using PyTorch. Each network
consisted of three hidden layers with 512, 1024, and 512 neurons,
respectively. The networks were trained over 1000 epochs, with a learning rate
of 0.0001 for the first 500 epochs and 0.00001 for the remaining 500 epochs.
We chose the Adam optimizer and batch size 16 for this task.

####  4.2.3 Stochastic-based molecule generator

Generative models have gained prominence as potent tools for the generation of
prospective new leads. Building upon our prior work, we introduced the
Generative Network Complex (GNC), a model specifically tailored to produce
novel, drug-like molecules [14]. To augment the efficacy of the GNC model, and
with guidance from GPT-4, we decided to integrate principles from diffusion-
based models [19, 31].

The Langevin equation is a stochastic differential equation (SDE) that used to
decribe diffusion processes. This equation encapsulates the random
trajectories of particles in their velocity space, accounting for both
deterministic and stochastic forces. A pivotal goal of this research is to
employ the Langevin equation suggested by ChatGPT as a mechanism to enhance
the molecular generator present in the GNC model.

Assume $\mathbf{X}$ is a latent space vector of a molecule with 512
dimensions, and $\mathbf{X}_{k}$ represents its $k$-th latent space reference
vector. Then the Langevin equation of our drug generator system is:

$\frac{d\mathbf{X}}{dt}=\alpha\sum_{k}a_{k}(\mathbf{X}_{k}-\mathbf{X})+\bm{\xi}(t),$
(1)

where $a_{k}$ is a positive weighting parameter corresponds to
$\mathbf{X}_{k}$ statisfying $\displaystyle\sum_{k}a_{k}=1$, $\bm{\xi}(t)$ is
a Gaussian white noise, and $\alpha$ is a hyperparameter. Then according to
the Langevin equation in the Supporting Information S1.1.4, the general
solution of this system is given by:

$\mathbf{X}(t)=\mathbf{C}^{-\alpha
t}+\int_{0}^{t}e^{-\alpha(t-u)}(\alpha\sum_{k}a_{k}\mathbf{X}_{k}+\bm{\xi}(u))du,$
(2)

where the initial state $\mathbf{X}(0)=\mathbf{C}$.

While the Langevin equation offers a microscopic depiction of the diffusion
process, a comprehensive understanding of the temporal evolution of particle
distribution requires a more macroscopic viewpoint. To bridge this gap, we
also introduce the Fokker-Planck equation. Derived from the Langevin equation
(a detailed derivation is available in Supporting Information S1.1.5), this
equation provide the connection between the dynamics of individual particles
and the overarching behavior of the entire system.

####  4.2.4 Drug screening

In drug discovery, several criteria are leveraged to filter promising drug
candidates. In our work, we consider molecules that fulfill the following
requirements as viable drug prospects: 1) exhibit favorable ADMET properties,
2) comply with Lipinski’s rule of five, 3) are synthetically accessible, 4)
possess proper physicochemical properties. These properties are crucial for
determining the drug-like nature and potential practical applicability of the
generated molecules, which will be elaborated on in the following paragraphs.

Table 3: The optimal ranges of 10 properties that are used to screen nearly optimal compounds, including seven selected ADMET properties, two physicochemical properties, and one medicinal chemistry properties. The seven ADMET properties include Caco-2 (the human colon adenocarcinoma cell lines) permeability, $\text{F}_{20\%}$ (the human oral bioavailability 20%), Pgp-sub (the substrate of P-glycoprotein), Pgp-inh (the inhibitor of P-glycoprotein), VD (volumn density), $\text{T}_{1/2}$ (The half-life of a drug), and FDAMDD (The maximum recommended daily dose). Moreover, SAS represents the synthetic accessibility score, log $P$ is the logarithm of the n-octanol/water distribution coefficient, and log $S$ indicates the logarithm of aqueous solubility value. Property | Profile | Excellent range | Medium range
---|---|---|---
Absorption | Caco-2 permeability |  $>$ -5.15 | /
Absorption | $\text{F}_{20\%}$ | 0 - 0.3 | 0.3 - 0.7
Absorption | Pgp-sub | 0 - 0.3 | 0.3 - 0.7
Absorption | Pgp-inh | 0 - 0.3 | 0.3 - 0.7
Distribution | VD | 0.04 - 20 L/kg | /
Excretion | $\text{T}_{1/2}$ | 0 - 0.3 | 0.3 - 0.7
Toxicity | FDAMDD | 0 - 0.3 | 0.3 - 0.7
Medicinal Chemistry | SAS |  $<$ 6 | /
Physicochemical | log $P$ | 0 - 3 log mol/L | /
Physicochemical | log $S$ | -4 - 0.5 log mol/L | /

First, undesirable pharmacokinetics and toxicity are leading causes of drug
development failure. Therefore, the assessment of absorption, distribution,
metabolism, excretion, and toxicity (ADMET) properties should occur as early
as possible in the drug development process. In this work, we applied ADMETlab
2.0 to offer us systematic evaluation of ADMET properties, along with certain
physicochemical properties and an assessment of medicinal chemistry
friendliness. In this work, we consider seven seven ADMET properties,
including Caco-2 (the human colon adenocarcinoma cell lines) permeability,
$\text{F}_{20\%}$ (the human oral bioavailability 20%), Pgp-substrate (the
substrate of P-glycoprotein), Pgp-inhibitor (the inhibitor of P-glycoprotein),
VD (volumn density), $\text{T}_{1/2}$ (The half-life of a drug), and FDAMDD
(The maximum recommended daily dose). The optimal range of these properties
are listed in Table 3.

Second, the Lipinski’s rule of five help to evaluate druglikeness or determine
if a chemical compound with a certain pharmacological or biological activity
has properties that would make it a likely orally active drug in humans, which
should satifies four physicochemical criteria: 1) molecular weight (MW) $\leq
500$ daltons, 2) octanol-water partition coefficient (log $P$) $\leq 5$, 3)
the number of hydrogen bond donors (nHD) $\leq 5$, 4) the number of hydrogen
bond acceptors (nHA) $\leq 10$.

Thirdly, synthetic accessibility is crucial to ensuring the feasibility of
large-scale production of a potential drug candidate. In this study, we used
RDKit to evaluate the synthetic accessibility score (SAS). A candidate drug
with an SAS score less than 6 indicates that it is relatively easy to
synthesize.

Lastly, physicochemical properties can significantly influence the solubility,
permeability, and stability of potential drug candidates. In this study, we
primarily focused on the logarithm of the n-octanol/water distribution
coefficient (log $P$) and the logarithm of aqueous solubility value (log $S$).
Drug candidates with a log $P$ in the range of 0 - 3 log mol/L and log $S$ in
the range of -4 - 0.5 log mol/L are considered to have suitable
physicochemical properties.

## Code and Data availability

The code and data are available at the public repository
https://github.com/wangru25/SGNC.

The datasets including SMILES strings and binding affinities of inhibitors
targeting DAT, NET, SERT. In addition, these datasets can also be found in the
’Training Datasets’ folder within the SupplementaryData.zip file, available
under Supporting Information S2 for readers interested in further exploration.

Trained models from this study are saved within the aforementioned code
repository. This repository includes the stochastic-based generative network
complex (SGNC) developed in Python, as well as Python scripts for calculating
reconstruction rates, evaluating synthetic accessibility, and generating
visual plots.

## Supporting Information

The Supporting Information is available for:

  * S1

Supplementary methods

    * S1.1

Fokker-Planck equation-embedded multi-target drug molecule generator

      * S1.1.1

Random variables

      * S1.1.2

Wiener process and white noise

      * S1.1.3

Itô’s lemma

      * S1.1.4

Langevin equation

      * S1.1.5

Derivation of Fokker-Planck equation from Langevin equation

    * S1.2

Evaluation metrics

  * S2

Supplementary Data: The SupplementaryData.zip consists 3 folders, namely
Training Datasets, Predictions, and Generated Molecules.

    * S2.1

Training Datasets: This folder has the datasets used for training purposes.

    * S2.2

Predictions: Within this folder, one can find data related to the predicted
binding affinity of inhibitors in 4 training datasets.

    * S2.3

Generated Molecules: This folder documents the molecules that have been
produced using the stochastic-based molecular generator.

  * S3

Supplementary Figures

    * S3.1

Radar plots of physicochemical properties for 15 lead candidates

    * S3.2

Molecular docking and molecular interaction of 15 leads with DAT and SERT

  * S4

Supplementary Dialogues

    * S4.1

The 1st persona of ChatGPT

    * S4.2

The 2nd persona of ChatGPT

    * S4.3

The 3rd persona of ChatGPT

    * S4.4

Other dialogues

## Acknowledgment

This work was supported in part by NIH grants R01GM126189, R01AI164266, and
R35GM148196, National Science Foundation grants DMS2052983, DMS-1761320, and
IIS-1900473, NASA grant 80NSSC21M0023, Michigan Economic Development
Corporation, Michigan State University Foundation, Bristol-Myers Squibb 65109,
and Pfizer.

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## Competing interests

The authors declare no competing interests.