Creation of targeted compound libraries based on 3D shape recognition.

In the emerging field of drug discovery, rapid virtual screening methods become extremely valuable, especially when dealing with ultra-large databases of organic small bioactive molecules. In this work, we present a fast, computationally resource-efficient, and simple workflow for screening targeted compound libraries generated from ultra-large virtual chemical space. This workflow aims to find compounds with similar molecular 3D shapes with reference ones, and at the same time to expand chemical diversity and to identify new and potentially active scaffolds. This pipeline ensures the enrichment of the generated libraries with novel chemotypes. Also, it was shown that delicate tailoring of the physicochemical parameters of the search set ensures that all library compounds will possess desired property distributions. A visual inspection has shown that found structures bind to the receptor in the same way as the reference ones. Using our screening workflow, we have created a number of conventional protein-targeted libraries: the GPCRs Targeted Library (531 K compounds) and the Protein Kinases Targeted Library (113 K compounds). The described pipeline and scripts are freely accessible at: https://github.com/ChemSpace-LLC/usrcat_sim .