Artificial Intelligence, Machine Learning, and Generative AI in Synthetic Planning and Drug Discovery

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have come up as transformative technologies in the pharmaceutical sciences, particularly in synthetic route planning and early-stage drug discovery.ML models can predict retrosynthetic pathways, optimize reaction conditions, and reduce trial-and-error in laboratory synthesis. Beyond their role in synthesis, AI-driven tools are now widely used to analyze complex biological and chemical data, helping researchers identify viable drug candidates at much earlier stages. Techniques such as virtual screening, predictive modeling, and structure–activity analysis allow scientists to make informed decisions before entering costly experimental phases. These methods also support the evaluation of safety, efficacy, and pharmacokinetic behavior, improving the overall quality of candidate selection. At the same time, Generative AI (GAI) is revolutionizing drug discovery by designing novel molecular structures with desired biological properties using deep learning algorithms such as generative adversarial networks (GANs) and transformer-based models. These intelligent systems not only accelerate the drug development timeline but also enhance decision-making in lead optimization and target validation. This integrated AI-driven approach significantly reduces cost, time and resources, enabling the way for faster and more efficient drug innovation.