文献类型：期刊文献

英文题名：Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

作者：Liu, Xiaofeng[1];Bai, Fang[2,4];Ouyang, Sisheng[1];Wang, Xicheng[2];Li, Honglin[1,2,3];Jiang, Hualiang[1,3]

机构：[1]Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China;[2]Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dept Engn Mech, Dalian 116023, Peoples R China;[3]E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China;[4]Dalian Univ Technol, Dept Chem Engn, Dalian 116012, Peoples R China

年份：2009

卷号：10

外文期刊名：BMC BIOINFORMATICS

收录：;EI(收录号：20131116099858);WOS:【SCI-EXPANDED(收录号:WOS:000266604200002)】；

基金：This work was supported by the National Natural Science Foundation of China (grants 20803022, 30672539 and 20721003), the Shanghai Committee of Science and Technology (Grant 07dz22004), the 863 Hi-Tech Program of China (Grant 2007AA02Z304), the International Collaboration Project of China (Grant 2007DFB30370) and the National Basic Research Program of China (Grants 2009CB918501 and 2009CB918502). H. L. was also supported by Knowledge Innovation Program of the Chinese Academy of Sciences (Grant SIMM0709QN-09). The authors thank Professor Deb for providing MOEA source code, Dr. Vainio for providing Balloon, Dr. Michael J. Potter from VeraChem for Vrms. We also thank Dr. Jiabo Li and Dr. Deqiang Zhang from Accelrys for their helpful discussions and suggestions.

语种：英文

外文关键词：Molecules - Evolutionary algorithms - Pareto principle - Stochastic systems

摘要：Background: Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results: The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput-Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 angstrom to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion: On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.