Integrated Molecular Modeling and Machine Learning for Drug Design

Song Xia, Eric Chen, Yingkai Zhang

Research output: Contribution to journalReview articlepeer-review


Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Original languageEnglish (US)
Pages (from-to)7478-7495
Number of pages18
JournalJournal of chemical theory and computation
Issue number21
StatePublished - Nov 14 2023

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry


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