A novel adaptive ensemble classification framework for ADME prediction. Issue 21 (26th March 2018)

• Record Type: Journal Article
• Authors: Yang, Ming; Chen, Jialei; Xu, Liwen; Shi, Xiufeng; Zhou, Xin; Xi, Zhijun; An, Rui; Wang, Xinhong
• Is Part Of: RSC advances. Volume 8:Issue 21(2018)
• Page Start: 11661
• Page End: 11683
• Publication Date: 2018-03-26
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c8ra01206g
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble. Abstract : It has now become clear that in silico prediction of ADME (absorption, distribution, metabolism, and elimination) characteristics is an important component of the drug discovery process. Therefore, there has been considerable interest in the development of in silico modeling of ADME prediction in recent years. Despite the advances in this field, there remains challenges when facing the unbalanced and high dimensionality problems simultaneously. In this work, we introduce a novel adaptive ensemble classification framework named as AECF to deal with the above issues. AECF includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble. We considered five sampling methods, seven base modeling techniques, and ten ensemble rules to build a choice pool. The proper route of constructing predictive models was determined automatically according to the imbalance ratio (IR). With the adaptive characteristics of AECF, it can be used to work on the different kinds of ADME data, and the balanced data is a special case in AECF. We evaluated the performance of our approach using five extensive ADME datasets concerning Caco-2 cell permeability (CacoP), human intestinal absorption (HIA), oral bioavailability (OB), and P-glycoprotein (P-gp) binders (substrates/inhibitors, PS/PI). The performance of AECF was evaluated on two independent datasets, and the average AUC values were 0.8574–0.8602, 0.8968–0.9182, 0.7821–0.7981, 0.8139–0.8311, and 0.8874–0.8898 for CacoP, HIA, OB, PS and PI, respectively. Our results show that AECF can provide better performance and generality compared with individual models and two representative ensemble methods bagging and boosting. Furthermore, the degree of complementarity among the AECF ensemble members was investigated for the purpose of elucidating the potential advantages of our framework. We found that AECF can effectively select complementary members to construct predictive models by our auto-adaptive optimization approach, and the additional diversity in both sample and feature space mainly contribute to the complementarity of ensemble members.