De Novo Design of Drug-Like Molecules by a Fragment-Based Molecular Evolutionary Approach

• Published in: Journal of chemical information and modeling Vol. 54; no. 1; pp. 49 - 56
• Main Authors: Kawai, Kentaro, Nagata, Naoya, Takahashi, Yoshimasa
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27.01.2014
• Subjects: Algorithms; Animals; Computational Biology; Computer Simulation; Databases, Pharmaceutical; Directed Molecular Evolution - statistics & numerical data; Drug Design; Drug Discovery - statistics & numerical data; Drugs; Humans; Ligands; Models, Chemical; Molecular Structure; Molecules; Mutation; Pharmaceuticals; Quantitative Structure-Activity Relationship; R&D; Rats; Receptor, Adenosine A2A - drug effects; Receptor, Serotonin, 5-HT1A - drug effects; Research & development
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/ci400418c

This paper describes a similarity-driven simple evolutionary approach to producing candidate molecules of new drugs. The aim of the method is to explore the candidates that are structurally similar to the reference molecule and yet somewhat different in not only peripheral chains but also their scaffolds. The method employs a known active molecule of our interest as a reference molecule which is used to navigate a huge chemical space. The reference molecule is also used to obtain seed fragments. An initial set of individual structures is prepared with the seed fragments and additional fragments using several connection rules. The fragment library is preferably prepared from a collection of known molecules related to the target of the reference molecule. Every fragment of the library can be used for fragment-based mutation. All the fragments are categorized into three classes; rings, linkers, and side chains. New individuals are produced by the crossover and the fragment-based mutation with the fragment library. Computer experiments with our own fragment library prepared from GPCR SARfari verified the feasibility of our approach to drug discovery.