De novo ligand evolution using bayesian optimization algorithms

Authors:
Masaharu Munetomo;Kiyoshi Akama;Haruki Maeda
Affiliations:
Hokkaido University, Information Initiative Center, Sapporo, Japan;Hokkaido University, Information Initiative Center, Sapporo, Japan;Hokkaido University, Graduate School of Information Science and Technology, Sapporo, Japan
Venue:
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Year:
2009

Citing 2
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Abstract

Ligand optimization by computer simulation is becoming popular in drug design process to reduce cost of the chemical experiments. This paper presents a novel approach generating optimal ligand structures from scratch employing Bayesian optimization algorithm to realize an automated design of drug and other chemical structures. The proposed approach based on de novo ligand evolution optimizes structure of ligands by adding fragments to the base structure employing Bayesian optimization algorithm which is a promising approach in probabilistic model-building genetic algorithms. We show the effectiveness of our approach compared to the conventional approach using classical genetic algorithms.