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

• URL: http://arxiv.org/abs/2308.06920v2
• Date: Thu, 19 Oct 2023 19:18:35 GMT
• Title: ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots
• Authors: Rui Wang, Hongsong Feng, Guo-Wei Wei
• Abstract summary: 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. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the development of potential pharmaceutical solutions.
• Score: 5.017265957266848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientific research is not clear yet. 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.

Related papers

• Drug Repurposing for Parkinson's Disease Using Random Walk With Restart Algorithm and the Parkinson's Disease Ontology Database [0.0]
  We design a novel computational platform that integrates gene expression data, biological networks, and the PDOD database to identify possible drug-repositioning agents for Parkinson's therapy. We propose gene expression analysis, network prioritization, and drug target data analysis to arrive at a comprehensive evaluation of drug repurposing chances.
  arXiv  Detail & Related papers  (2024-04-11T20:11:25Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Artificial Intelligence for Drug Discovery: Are We There Yet? [0.08306867559432653]
  Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs.
  arXiv  Detail & Related papers  (2023-07-13T01:51:26Z)
• CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery [0.32634122554914]
  CardiGraphormer is a novel combination of Graphormer and Cardinality Preserving Attention. SSL performs to learn potent molecular representations and employs GNNs to extract molecular fingerprints. CardiGraphormer's potential applications in drug discovery and drug interactions are vast.
  arXiv  Detail & Related papers  (2023-07-03T08:58:32Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design [133.1268990638971]
  De novo drug design based on the structure of a target protein can provide novel drug candidates. We present a generative solution named TamGent that can directly generate candidate drugs from scratch for a given target.
  arXiv  Detail & Related papers  (2022-08-30T09:32:39Z)
• Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
  Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
  arXiv  Detail & Related papers  (2022-02-08T09:42:08Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)
• Artificial Intelligence in Drug Discovery: Applications and Techniques [33.59138543942538]
  Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. We first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks. We then discuss common data resources, molecule representations and benchmark platforms.
  arXiv  Detail & Related papers  (2021-06-09T20:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.