Development and experimental validation of a machine learning model for the prediction of new antimalarials.

A large set of antimalarial molecules (N ~ 15k) was employed from ChEMBL to build a robust random forest (RF) model for the prediction of antiplasmodial activity. Rather than depending on high throughput screening (HTS) data, molecules tested at multiple doses against blood stages of Plasmodium falciparum were used for model development. The open-access and code-free KNIME platform was used to develop a workflow to train the model on 80% of data (N ~ 12k). The hyperparameter values were optimized to achieve the highest predictive accuracy with nine different molecular fingerprints (MFPs), among which Avalon MFPs (referred to as RF-1) provided the best results. RF-1 displayed 91.7% accuracy, 93.5% precision, 88.4% sensitivity and 97.3% area under the Receiver operating characteristic (AUROC) for the remaining 20% test set. The predictive performance of RF-1 was comparable to that of the malaria inhibitor prediction platform (MAIP), a recently reported consensus model based on a large proprietary dataset. However, hits obtained from RF-1 and MAIP from a commercial library did not overlap, suggesting that these two models are complementary. Finally, RF-1 was used to screen small molecules under clinical investigations for repurposing. Six molecules were purchased, out of which two human kinase inhibitors were identified to have single-digit micromolar antiplasmodial activity. One of the hits (compound 1) was a potent inhibitor of β-hematin, suggesting the involvement of parasite hemozoin (Hz) synthesis in the parasiticidal effect. The training and test sets are provided as supplementary information, allowing others to reproduce this work.