Division/Committee: [COMP: Division of Computers in Chemistry]

Coarse-grain modeling represents a cutting-edge approach in both drug discovery and formulation. By simplifying molecular structures while preserving essential interactions, coarse-grain modeling enables researchers to simulate large-scale systems efficiently. This method may facilitate the exploration of drug-protein interactions and the prediction of new binding pockets and binding affinities, which are crucial for drug discovery efforts. Additionally, coarse-grain modeling has shown potential in aiding predictions related to drug/peptide membrane permeability and solubility. Furthermore, in drug formulation, coarse-grain modeling informs the design of delivery systems by elucidating molecular self-assembly, optimizing compositions and drug release profiles. Its integration with all-atom models bridges the gap between detailed molecular understanding and system-level properties, allowing for a comprehensive exploration of drug behavior. The combination of coarse-grained models with machine learning also offers a promising avenue for enhancing these techniques. This synergy enables the development of predictive models that capture both the efficiency of coarse-grained modeling and the accuracy of machine learning algorithms. In essence, coarse-grain and multiscale modeling techniques, coupled with machine learning, offer a powerful toolkit for advancing therapeutics and optimizing drug formulations.

Jianing Li

Purdue University

Appearances