Benchmarking the Structure-Based Virtual Screening Performance of Wild-Type and Resistant PfDHFR Using Docking and Machine Learning Re-Scoring
Abstract
PURPOSE: Malaria, caused by the parasite Plasmodium falciparum, remains a critical health challenge. Its enzyme Dihydrofolate Reductase (PfDHFR) is vital for the parasite's survival and a key target for antimalarial drugs. Mutations in PfDHFR are primary cause of drug resistance in malaria parasites, particularly to antifolate drugs, like pyrimethamine. Herein, a comprehensive structure-based virtual screening (SBVS) benchmarking analysis of three generic docking tools against both wild-type (WT) and quadruple-mutant (Q) PfDHFR variants were investigated. Furthermore, re-scoring of the docking outcome via two popular pretrained machine learning scoring functions (ML SFs) were explored. The study provides valuable recommendations into enhancing the SBVS performance against both the WT and the resistant Q PfDHFR variants.
METHODS: Three generic docking tools (AutoDock Vina, PLANTS, and FRED) were evaluated using the DEKOIS 2.0 benchmark set against both WT and Q PfDHFR variants. Furthermore, we analyzed the re-scoring performance of two pretrained ML SFs, namely CNN-Score and RF-Score-VS v2. In depth analysis of the screening performance and enrichment behavior using pROC-AUC, pROC-Chemotype plots and EF 1% were deliberated.
RESULTS: Overall, eighteen docking and re-scoring outcomes for both variants were conducted. For the WT PfDHFR, PLANTS demonstrated the best enrichment when combined with CNN re-scoring reflecting an EF 1% value of 28. Re-scoring with RF and CNN…
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