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

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 Plasmo...

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Bibliographic Details
Main Authors: Mukul Kore, Dimple Acharya, Lakshya Sharma, Shruthi Sridhar Vembar, Sandeep Sundriyal
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Chemistry
Subjects:
Malaria
Machine learning
Random forest
KNIME
Modelling
ChEMBL
Online Access:https://doi.org/10.1186/s13065-025-01395-4
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