Improvement of the structure generation algorithm for exploring chemical space toward wider diversity of generated structures

Abstract

The use of structural generators is one of the ways to develop efficiently functioning organic molecules. Here we present a novel algorithm to diversify the structure generated by the DAECS structure generator, which were previously developed to generate structures having objective properties. The proposed algorithm was implemented for seed structure selection by restricting the search area and then clustering the structure on the 2D map generated by the GTM algorithm. To evaluate our algorithm, we conducted a computational experiment using ligand-like structures for the histamine H1 receptor. While there is still room for improvement, our algorithm is superior to previous methods in terms of structural diversity: the structures generated by our algorithm were more interspersed on the 2D map and their average Tanimoto distance was longer compared with those generated by the previous algorithm. It was also proven that the proposed algorithm was efficient in terms of computational cost.