Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods

Drug design involves the process of identifying and designing novel molecules that have desirable properties and bind well to a given target receptor. Typically, such molecules are identified by screening large chemical libraries for desirable physicochemical properties and binding strength with the target protein. This traditional approach, however, has severe limitations as exhaustively screening every molecule in known chemical libraries is computationally infeasible. Furthermore, currently available molecular libraries are only a minuscule part of the entire set of possible drug-like molecular structures (drug-like chemical space). In this review, we discuss how the former limitation is addressed by modeling virtual screening as a search space problem and how these endeavors utilize machine learning to reduce the number of required computational experiments to identify top candidates. We follow that up by discussing generative methods that attempt to approximate the entire drug-like chemical space providing us a path to explore beyond the known drug-like chemical space. We place special emphasis on generative models that learn the marginal distributions conditioned on specific properties or receptor structures for efficient sampling of molecules. Through this review, we aim to highlight modern machine learning based methods that try to efficiently enhance our sampling capability beyond conventional screening methods which, in turn, would benefit drug design significantly. Therefore, we also encourage further methods of development that work on such important aspects of drug design. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning Data Science > Computer Algorithms and Programming

Automated summary

Drug design involves identifying and designing new molecules that have desirable properties and bind well to target receptors. Traditional methods have limitations, leading researchers to model virtual screening as a search space problem and use machine learning to reduce computational experiments. Generative methods approximate the entire drug-like chemical space and focus on efficient molecule sampling based on specific properties or receptor structures. This review highlights the importance of modern machine learning methods in enhancing sampling capability beyond conventional screening methods and encourages further development in addressing key aspects of drug design.