ABSTRACT:
Artificial intelligence (AI) has rapidly become a paradigm shift in driving in-silico research to provide new solutions to old drug discovery and development challenges. AI facilitates fast design of drugs, precise prediction of drug-target interactions, better Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiling and more toxicological screenings by using machine learning, deep learning and generative algorithms. The combination of AI and molecular modeling, simulations, and multi-omics platforms will guarantee accuracy and customization in the development of therapies never previously achieved. Also, AI helps in automation, feature extraction, and natural language processing and may increase the reproducibility and broaden the nature of biomedical research. Although these improvements have been made, several challenges are still present, especially the quality of data, interpretability, ethical concerns, validation systems, and generalizability in non-homogenous populations and in rare diseases. These issues are central to the realization of the potential of AI in precision medicine. Explainable AI, federated learning, quantum computing and regulatory alignment will be the future of AI in healthcare. Finally, AI needs to be used to support instead of supplanting human expertise, supporting informed, patient-centered decision making and generating innovative, ethical healthcare solutions.
Cite this article:
Prasad Hemant Mahajan, Mahalaxmi Mohan. Advancing Drug Discovery via AI and In Silico Approaches. Asian Journal of Pharmaceutical Research. 2026; 16(4):487-1. doi: 10.52711/2231-5691.2026.00070
Cite(Electronic):
Prasad Hemant Mahajan, Mahalaxmi Mohan. Advancing Drug Discovery via AI and In Silico Approaches. Asian Journal of Pharmaceutical Research. 2026; 16(4):487-1. doi: 10.52711/2231-5691.2026.00070 Available on: https://www.asianjpr.com/AbstractView.aspx?PID=2026-16-4-17
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