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This review aims to explore the role of AI and ML in transforming drug design and discovery processes.

May 1, 2026Open Access

Artificial Intelligence and Machine Learning in Drug Design and Discovery: A Comprehensive Analysis

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This review aims to explore the role of AI and ML in transforming drug design and discovery processes.
Review of AI applications in the drug discovery pipeline including target identification and virtual screening.
Discussion of advanced models such as graph neural networks and transformers for improving drug design.
Examination of clinical examples and emerging approaches like federated learning and quantum machine learning.
AI has significantly improved molecular representation and novel compound generation.
Challenges such as model interpretability and data bias continue to hinder AI's full potential.
Interdisciplinary collaboration is crucial for the safe implementation of AI in pharmaceutical development.

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Comprehensive analysis reveals AI enhances drug discovery in pharmaceuticals, indicating pivotal advancements.

*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya (2026) studied this question.

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