Drug Development and Discovery Considering Artificial Intelligence: A Through Analysis
Year: 2025
Type: article
Abstract: Artificial Intelligence (AI) is increasingly reshaping drug discovery and development by offering new computational capabilities that significantly enhance efficiency, accuracy, and innovation. This comprehensive review discusses the evolving role of AI across various stages of pharmaceutical R&D—from early target identification and validation to lead optimization, preclinical assessment, and clinical trials. With the growing complexity and costs associated with traditional drug development pipelines, AI presents powerful alternatives through machine learning (ML), deep learning (DL), and natural language processing (NLP) tools that enable rapid data analysis, compound generation, and predictive modeling. In target discovery, AI algorithms analyze vast omics datasets to identify novel biological targets, while virtual screening models streamline high-throughput screening of chemical libraries with improved hit rates. Lead optimization benefits from AI’s ability to predict ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, thus reducing the failure rate in later stages. In clinical research, AI assists in patient stratification, real-time monitoring, and biomarker identification, accelerating trial timelines and enhancing patient safety. This review evaluates prominent AI platforms such as Deep Chem, Atom Net, Schrödinger’s suite, and AlphaFold, along with case studies from industry leaders like Pfizer, Novartis, and Insilco Medicine. Challenges such as data quality, model interpretability, algorithmic bias, and regulatory concerns are critically analyzed. The paper concludes by identifying future research opportunities and emphasizing the need for collaborative frameworks between AI developers, biologists, and regulatory bodies. As AI continues to evolve, its integration into the drug discovery lifecycle holds the promise of significantly transforming pharmaceutical innovation, enabling more targeted therapies and advancing the vision of precision medicine.
Author: Sachin Verma*1, Ridam Gaud2, Ankita Bhattacharjee3, Sadia Parveen4
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082371189
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