title: Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.
abstract: In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.
authors.affiliation: Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam.
authors.forename: Hai
authors.initials: H
authors.lastname: Pham-The
authors.affiliation: Departament de Bioquímica i Biologia Molecular, Universitat de València, Burjassot, 46100, Valencia, Spain.|Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain.|Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Paso lateral km 2 1/2 via Napo, Puyo, Ecuador.
authors.forename: Gerardo
authors.initials: G
authors.lastname: Casañola-Martin
authors.affiliation: Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot, 46100, Valencia, Spain.
authors.forename: Teresa
authors.initials: T
authors.lastname: Garrigues
authors.affiliation: Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d'Alacant, Alicante, Spain.
authors.forename: Marival
authors.initials: M
authors.lastname: Bermejo
authors.affiliation: Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d'Alacant, Alicante, Spain.
authors.forename: Isabel
authors.initials: I
authors.lastname: González-Álvarez
authors.affiliation: Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam.
authors.forename: Nam
authors.initials: N
authors.lastname: Nguyen-Hai
authors.affiliation: Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot, 46100, Valencia, Spain.|Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d'Alacant, Alicante, Spain.|Unit of Modeling and Experimental Biopharmaceutics, Chemical Bioactive Center, Central University of Las Villas, 54830, Santa Clara, Villa Clara, Cuba.
authors.forename: Miguel Ángel
authors.initials: MÁ
authors.lastname: Cabrera-Pérez
authors.affiliation: School of Medicine and Pharmacy, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam. ltthuong1017@gmail.com.
authors.forename: Huong
authors.initials: H
authors.lastname: Le-Thi-Thu
mesh_terms: D018938:Caco-2 Cells; D016208:Databases, Factual; D006801:Humans; D000069550:Machine Learning; D008954:Models, Biological; D010539:Permeability; D060388:Support Vector Machine
keywords: ADME modeling; Biopharmaceutics classification system; Caco-2 cell permeability; Cost-sensitive learning; Resampling technique; Support vector machine