Ligand Efficiency Outperforms pIC50 on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists. (15th October 2015)

• Record Type: Journal Article
• Authors: Li, Jiazhong; Bai, Fang; Liu, Huanxiang; Gramatica, Paola
• Is Part Of: Chemical biology & drug design. Volume 86:Number 6(2015:Dec.)
• Page Start: 1501
• Page End: 1517
• Publication Date: 2015-10-15
• Subjects: androgen receptor antagonist -- comparative molecular field analysis -- ligand efficiency -- multiple linear regression -- surface efficiency index; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.12619
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract : The concept of ligand efficiency is defined as biological activity in each molecular size and is widely accepted throughout the drug design community. Among different LE indices, surface efficiency index (SEI) was reported to be the best one in support vector machine modeling, much better than the generally and traditionally used end‐point pIC50 . In this study, 2D multiple linear regression and 3D comparative molecular field analysis methods are employed to investigate the structure–activity relationships of a series of androgen receptor antagonists, using pIC50 and SEI as dependent variables to verify the influence of using different kinds of end‐points. The obtained results suggest that SEI outperforms pIC50 on both MLR and CoMFA models with higher stability and predictive ability. After analyzing the characteristics of the two dependent variables SEI and pIC50, we deduce that the superiority of SEI maybe lie in that SEI could reflect the relationship between molecular structures and corresponding bioactivities, in nature, better than pIC50 . This study indicates that SEI could be a more rational parameter to be optimized in the drug discovery process than pIC50 . Abstract : We expanded the application of the ligand efficiency SEI to MLR and CoMFA modeling to investigate the relationships between the hydantoin derivatives and AR antagonist activities. The obtained results indicate that the SEI‐based models outperform the pIC50 ‐based models with higher stability, robustness, and predictive abilities. We put forward our opinion that SEI can incarnate the relationships between bioactivities and molecular structures better than pIC50, in nature. SEI could be a more rational parameter to be optimized in the drug discovery.