The development of novel organic compounds with desired properties is time consuming and costly. Thus, the quantitative structure-property relationship (QSPR) model is used widely for efficiently discovering compounds with the desired properties. Novel structures can be generated from a variety of input structures in silico by structure generators. We previously developed the structure generator DAECS to yield highly active drug-like structures. However, the structural diversity of the structures generated by DAECS was still small for practical applications such as drug discovery. In this paper, we present structure modification rules and the algorithm to output more diverse structures through the DAECS workflow. Two new types of structural modification rules, bond contraction and ring mergence, were added. The new algorithm, which restricts the search area and subsequently clusters structures on a two-dimensional map generated by generative topographic mapping, was implemented for the repetitive selection of seed structures. A case study was conducted to evaluate our method using ligand structures for the histamine H1 receptor. The results showed improved structural diversity than the previous method.
Keywords: Chemoinformatics; Molecular diversity; Structure generation; Structure-activity relationships; Virtual screening.
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