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

ABSTRACT: 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

SUBMITTER: Kore M 

PROVIDER: S-EPMC11783816 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature