The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 29-177
Full Name: Masaharu Munetomo
Position: Associate Professor
Age: ON
Sex: Male
Address: North 11, West 5, Kita-ku, Sapporo, Hokkaido
Country: JAPAN
Tel: +81-11-706-3759
Tel prefix:
Fax: +81-11-706-3759
E-mail address: munetomo@iic.hokudai.ac.jp
Other E-mails: munetomo@cims.hokudai.ac.jp
Title of the Paper: An Automated Ligand Evolution System using Bayesian Optimization Algorithm
Authors as they appear in the Paper: Masaharu Munetomo, Haruki Maeda, Kiyoshi Akama
Email addresses of all the authors: munetomo@iic.hokudai.ac.jp,maeda@uva.cims.hokudai.ac.jp
Number of paper pages: 10
Abstract: Ligand docking checks whether a drug chemical called ligand matches the target receptor protein of human organ or not. Docking by computer simulation is becoming popular in drug design process to reduce cost and time of the chemical experiments. This paper presents a novel approach generating optimal ligand structures from scratch based on de novo ligand design approach employing Bayesian optimization algorithm to realize an automated design of drug and other chemical structures. The proposed approach searches an optimal structure of ligand that minimizes bond energy to the receptor protein, and the structure of ligand is generated by adding small fragments of molecules to the base structure. The decision of adding fragments are controlled by Bayesian optimization algorithm which is considered as a promising approach in probabilistic model-building genetic algorithms. We have built a system that automatically generates an optimal structure of ligand, and through nu!
merical experiments performed on a PC cluster, we show the effectiveness of our approach compared to the conventional approach using classical genetic algorithms.
Keywords: Automated drug design, Ligand docking, Screening, De novo ligand design approach, Probabilistic model-building genetic algorithms, Estimation of distribution of algorithms, Bayesian optimization algorithms
EXTENSION of the file: .pdf
Special (Invited) Session: De Novo Ligand Evolution using Bayesian Optimization Algorithms
Organizer of the Session: 699-131
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