Development of de novo design method generating diverse structures in a target area on chemical space

Abstract

For drug development, methods of constructing a library consisting of diverse ligand candidates are required. We focused on de novo design algorithm for exploring chemical space (DAECS) which is the method generating many structures in a selected target area on the chemical space, and improved it. DAECS can generate only structures that exist in a specific area on a subspace set by using ligands data. But it is impossible to consider properties other than the activity and ensure the diversity of generated structures in DAECS. In this study, we introduce an area selection method with the visualization of drug-likeness distribution of the chemical space and a structural conversion method using substructures for solving the problem. To confirm superiority of our methods over the prior study, we performed a case study using a data set of ligands for human adrenergic alpha2A receptors from GVK database and showed the proposed methods can generate more diverse structures on a selected area and generate structures considering their drug-likeness.