Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods.

ABSTRACT: The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly pr

SUBMITTER: Sauer S 

PROVIDER: S-EPMC9629386 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature