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

arXiv: https://arxiv.org/abs/2212.08104 · HTML: https://mlsanity.com/paper/2212.08104/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 65 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)

## Built on by (most cited first, 20 of 20)

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- [SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research](https://mlsanity.com/paper/2308.13149/) (2023)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [MyCrunchGPT: A chatGPT assisted framework for scientific machine learning](https://mlsanity.com/paper/2306.15551/) (2023)
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- [3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery](https://mlsanity.com/paper/2502.05107/) (2025)
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- [A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives](https://mlsanity.com/paper/2506.16594/) (2025)
- [Science in the Era of ChatGPT, Large Language Models and Generative AI: Challenges for Research Ethics and How to Respond](https://mlsanity.com/paper/2305.15299/) (2023)
- [DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model](https://mlsanity.com/paper/2508.18579/) (2025)
- [A Survey for Large Language Models in Biomedicine](https://mlsanity.com/paper/2409.00133/) (2024)
- [Foundation Models for AI-Enabled Biological Design](https://mlsanity.com/paper/2505.11610/) (2025)
- [Out-of-Support Generalisation via Weight-Space Sequence Modelling](https://mlsanity.com/paper/2602.13550/) (2026)
- [Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling](https://mlsanity.com/paper/2510.22123/) (2025)

## 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.