Artificial Intelligence in Drug Discovery: A Comprehensive Review

DOI

10.2991/978-94-6463-876-9_15How to use a DOI?

Keywords

Artificial Intelligence; Drug Discovery; Machine Learning; Deep Learning; Predictive Toxicology; Clinical Trials; Generative Models; Target Identification; Molecular Design; Quantum Computing; Federated Learning

Abstract

Artificial Intelligence (AI) is transforming the landscape of drug discovery and development. Traditional approaches, though foundational, are often limited by high costs, lengthy timelines, and high attrition rates in clinical trials. Recent advances in machine learning (ML), deep learning (DL), and natural language processing (NLP) have enabled AI to introduce exceptional speed, efficiency, and accuracy into pharmaceutical research. AI-driven methodologies facilitate target identification by extracting insights from complex biological datasets, optimize molecular design through predictive modeling, and improve clinical trial outcomes via patient stratification and adaptive trial designs. This review offers a comprehensive examination of AI applications across the drug discovery pipeline, including target identification, lead optimization, predictive toxicology, and illustrative real-world case studies. Key challenges—such as data quality, model interpretability, and ethical considerations—are critically discussed. The review concludes by highlighting future directions, including the integration of emerging technologies like quantum computing and federated learning, and underscores the importance of interdisciplinary collaboration in advancing the field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Sudarshana Santhosh Kumar Kothapalli
AU  - Dipak Kumar Sahoo
AU  - T. Santhosh Kumar
AU  - Suman Chirra
PY  - 2025
DA  - 2025/10/23
TI  - Artificial Intelligence in Drug Discovery: A Comprehensive Review
BT  - Proceedings of the International Conference on Sustainable Science and Technology for Tomorrow (SciTech 2024)
PB  - Atlantis Press
SP  - 180
EP  - 200
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6463-876-9_15
DO  - 10.2991/978-94-6463-876-9_15
ID  - Kothapalli2025
ER  -