Artificial Intelligence in Chemistry: Machine Learning Approaches for Drug Discovery, Molecular Design and Chemical Prediction

Abstract

Artificial intelligence (AI) and machine learning (ML) have emerged as important computational approaches in modern chemistry, providing new methods for analyzing chemical data, predicting molecular properties, discovering bioactive compounds, designing novel molecules, and optimizing chemical reactions. Traditional chemical research has frequently relied on experimental trial and error, expert knowledge, theoretical calculations, and extensive laboratory screening. Although these approaches remain essential, the enormous size of chemical space and the increasing availability of chemical, biological, and structural data have created opportunities for data-driven computational methods. Machine learning can identify complex relationships between molecular structure and chemical or biological properties and can subsequently use these relationships to make predictions for previously untested compounds. In drug discovery, AI-assisted approaches can support target identification, virtual screening, quantitative structure–activity relationship modeling, molecular property prediction, de novo molecular design, and optimization of lead compounds. Generative models, including recurrent neural networks, were already being explored by 2017 for generating focused molecular libraries with desirable biological properties. This paper examines the foundations of AI in chemistry, major machine learning methodologies, molecular representations, applications in drug discovery and molecular design, chemical property and reaction prediction, advantages, limitations, and future prospects. The discussion emphasizes developments available up to 2017 and highlights the transition toward increasingly automated and data-driven chemical research.