Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

Fang Bai,Xicheng Wang,Xiaofeng Liu,Honglin Li,Hualiang Jiang,Haoyun Zhang,Jiabo Li

doi:10.1186/1471-2105-11-545

Abstract

BackgroundConformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.ResultsOur analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.ConclusionsBy incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

Highlights

Conformational sampling for small molecules plays an essential role in drug discovery research pipeline
Performance of the conformational search methods To assess the performance of force field based method (FFBM) and multiple empirical criteria based method (MECBM), the cumulative distributions of root mean square deviation (RMSD) between the bioactive conformers and their best fitting conformers in the generated conformer ensembles for each conformational search methods are presented in Figure 1 and Table 1
About 25% of the bioactive conformations can be reproduced by MECBM with RMSD falling within 00.5 Å RMSD interval, while only about 15% of the bioactive conformations can be reproduced by FFBM with RMSD falling within the same interval

Summary

Introduction

Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Conformational sampling, as an essential part of the molecular modelling process, is an important prerequisite for many applications in computational chemistry [1,2,3,4], especially in drug discovery research[5,6,7,8,9]. It has been a research hotspot in the last decades and many computational algorithms have appeared in succession recently[3,5,10,11,12,13,14,15,16,17]. The more common ways to avoid combinatorial explosion are the stochastic methods, such as random search[23] and molecular dynamics [24], which rely on certain random perturbations but usually still spend considerable computational time on energy minimization developing a fast and reliable conformational generation engine is still a challenging task[10,11]

Methods

Results

Discussion

Conclusion