Development and rigorous validation of antimalarial predictive models using machine learning approaches. Issue 8 (3rd August 2019)

• Record Type: Journal Article
• Authors: Danishuddin, ; Madhukar, G.; Malik, M.Z.; Subbarao, N.
• Is Part Of: SAR and QSAR in environmental research. Volume 30:Issue 8(2019)
• Page Start: 543
• Page End: 560
• Publication Date: 2019-08-03
• Subjects: Antimalarial -- predictive models -- machine learning -- calibration -- predictiveness curve; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2019.1635526
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum . Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using the diverse antimalarial dataset from ChEMBL. A well-established feature selection framework was used to select the best subset from a larger pool of descriptors. Performance of the models was rigorously evaluated by evaluation of the applicability domain, Y-scrambling and AUC-ROC curve. Additionally, the predictive power of the models was also assessed using probability calibration and predictiveness curves. SVM and XGBoost showed the best performances, yielding an accuracy of ~85% on the independent test set. In term of probability prediction, SVM and XGBoost were well calibrated. Total gain (TG) from the predictiveness curve was more related to SVM (TG = 0.67) and XGBoost (TG = 0.75). These models also predict the high-affinity compounds from PubChem antimalarial bioassay (as external validation) with a high probability score. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery of antimalarial agents.