Compound property enhancement by virtual compound synthesis

Abstract

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning

“…Another advantage is the ability to perform interaction analysis using the docked structures, which provides knowledge of protein−ligand binding 15 to understand the affinity 16 and selectivity 17,18 of the compounds. These analyses can also be used to discover new chemical compounds, by generating derivatives systematically 19 and evaluating these compounds with structure-based scoring. By contrast, the scoring function in typical LBVS is based on the similarity of the chemical structures.…”

Predicting Strategies for Lead Optimization via Learning to Rank

“…An MMP is a pair of compounds that differing in only in one part of their chemical structure [16], and MMPs have previously been used for ADME prediction [17] and compound optimization [18]. Chemical reaction-based method simulates virtual chemical re- actions to generate new compounds, and have previously been used for compound optimization [15]. As this method uses practical chemical reactions for exploration, generated compounds are more synthesizable than MMP-based systems.…”

“…When new hit compound are input, the two modules are iteratively applied: exploration module and evaluation module. In exploration module, modified compounds are virtually explored from input compounds using virtual compound optimization system[15]. In evaluation module, input compounds are evaluated by learned strategy.…”