Volume 10 Number 2 2026
Computational ADMET Profiling in Antimalarial Drug Development: Progress, Platforms, and the PBPK Frontier
Muhit Rana 1*, Md Nafiujjaman 2
Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210964
Submitted: 04 March 2026 Revised: 20 April 2026 Accepted: 01 May 2026 Published: 03 May 2026
Malaria still kills more than half a million people a year, and the chemical arsenal against Plasmodium falciparum is thinner than the case numbers suggest. Resistance has eroded the frontline regimens, yet replacing them is slow: a single new chemical entity takes ten to fifteen years and upwards of USD 2.5 billion, and roughly nine in ten candidates entering clinical testing never reach approval. Potency is rarely the culprit. Failure arrives later, as poor absorption, rapid hepatic clearance, hERG-mediated cardiotoxicity, or genotoxicity no early cell assay flagged. This review examines how machine learning has changed the timing of that reckoning. We trace the shift from linear QSAR and rule-of-five filters toward random forests, gradient-boosted ensembles, graph neural networks and transformer encoders, and assess the servers that made these methods usable at the bench — SwissADME, pkCSM and Deep-PK, ADMETlab 3.0, ProTox-II, eToxPred, DeepTox, admetSAR 2.0 and AutoFilter. Four target campaigns — PfLDH, apicoplast DNA polymerase, the FAS-II enzymes ACC and FabI, and 4-aminoquinoline optimisation — are synthesised to show what integrated screening delivers, and where it does not. Persistent constraints follow: sparse and biased training data, opaque deep models, narrow applicability domains, and a stubborn gap between endpoint-by-endpoint prediction and whole-organism pharmacokinetics. Coupling machine learning to mechanistic PBPK modelling appears, for now, the most credible route across that gap.
Keywords: ADMET prediction; machine learning; antimalarial drug discovery; Plasmodium falciparum; late-stage attrition; in silico toxicology
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