mlsanityData to 7 Oct 2026

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

arXiv 2212.08104 ↗First posted 8 Dec 2022cs.CL cs.AI cs.CYcited by 20

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Abstract (arXiv)

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human-authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, to assist human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 3 of its 65 references that are in the map. First posted on arXiv 8 Dec 2022.

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3 of 65 references.

References · 3

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  3. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 3; 2025: 7; 2026: 2.

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  1. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  2. 2023Gender bias and stereotypes in Large Language Modelscited by 185
  3. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  4. 2023SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Researchcited by 112
  5. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  6. 2023MyCrunchGPT: A chatGPT assisted framework for scientific machine learningcited by 8
  7. 2023Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Reviewcited by 8
  8. 2023DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentationcited by 7
  9. 2024An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learningcited by 5
  10. 20253DMolFormer: A Dual-channel Framework for Structure-based Drug Discoverycited by 4
  11. 2025Learning Sparsity for Effective and Efficient Music Performance Question Answeringcited by 4
  12. 2024Protected group bias and stereotypes in Large Language Modelscited by 3
8 more
  1. 2025A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectivescited by 3
  2. 2026Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materialscited by 3
  3. 2023Science in the Era of ChatGPT, Large Language Models and Generative AI: Challenges for Research Ethics and How to Respondcited by 2
  4. 2025DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Modelcited by 2
  5. 2024A Survey for Large Language Models in Biomedicinecited by 1
  6. 2025Foundation Models for AI-Enabled Biological Designcited by 1
  7. 2026Out-of-Support Generalisation via Weight-Space Sequence Modellingcited by 1
  8. 2025Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modelingcited by 0