LEVERAGING ARTIFICIAL INTELLIGENCE IN MEDICINAL CHEMISTRY: ADVANCES IN DRUG SYNTHESIS, DEVELOPMENT AND DESIGN

MANJIRI SHASTRI; SIDDHANT RAUT; VEDANT TAKALKAR; SWATI KSHIRSAGAR

doi:10.22159/ijpps.2025v17i11.56256

Authors

MANJIRI SHASTRI JSPM’s Rajarshi Shahu College of Pharmacy and Research, Tathawade, Pune-411033, India https://orcid.org/0009-0000-9868-409X
SIDDHANT RAUT JSPM’s Rajarshi Shahu College of Pharmacy and Research, Tathawade, Pune-411033, India https://orcid.org/0009-0002-4961-0494
VEDANT TAKALKAR JSPM’s Rajarshi Shahu College of Pharmacy and Research, Tathawade, Pune-411033, India https://orcid.org/0009-0008-5272-8366
SWATI KSHIRSAGAR JSPM’s Rajarshi Shahu College of Pharmacy and Research, Tathawade, Pune-411033, India https://orcid.org/0000-0001-6322-7032

DOI:

https://doi.org/10.22159/ijpps.2025v17i11.56256

Keywords:

Artificial intelligence, Productivity, Synthesis, Development, Designing, Antitubercular drug discovery

Abstract

Artificial intelligence (AI) has transitioned from a prospective innovation to an integral component of current pharmaceutical research and development. The objective of the present review is to explore the study of recently developed AI techniques that focus on enhancing human intelligence functions rather than simply imitating them. With the help of AI and Robotics, the most desirable components of industry, such as increased productivity, consistency in product quality, and the production of goods including hazardous reactants without danger to workers' lives, can be achieved. AI in drug synthesis can be applied for automated reaction prediction, retrosynthesis planning, and process optimization. As for drug design, different AI models can be used for de novo drug design, ligand and structure-based and multi-target drug design. AI, in predictive modeling, can be used for quantitative structure-activity relationship (QSAR) modeling, predictive toxicology, absorption, distribution, metabolism, excretion, and toxicity (ADMET) Profiling, and disease modeling. AI with robotics in the life of mankind has several advantages and disadvantages. Overall, AI-driven tools in drug synthesis, molecular design, and predictive modeling streamline the drug discovery process, enabling medicinal chemists to innovate more quickly and precisely. These applications help move from a trial-and-error approach to a data-driven one, ultimately bringing more effective therapies to market. The future is always hard to predict, but it will be determined by AI as it will become the next frontier in pharmacy.This review primarily focuses on the application of AI in Medicinal Chemistry, encompassing drug synthesis, development, and design.

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