Benchmarking the Structure-Based Virtual Screening Performance of Wild-Type and Resistant PfDHFR Using Docking and Machine Learning Re-Scoring

Menna S Hany,1 Nermin S Ahmed,2 Frank M Boeckler,3,4 Tamer M Ibrahim5,6 1Pharmaceutical Chemistry Department, Faculty of Biotechnology, German International University, Cairo, Egypt; 2Pharmaceutical Chemistry Department, Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, Egypt; 3Laboratory for Molecular Design & Pharmaceutical Biophysics, Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Eberhard Karls Universität Tübingen, Tuebingen, 72076, Germany; 4Interfaculty Institute for Biomedical Informatics (IBMI), Eberhard Karls Universität Tübingen, Tübingen, 72076, Germany; 5Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt; 6Center for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, EgyptCorrespondence: Tamer M Ibrahim, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt, Email [email protected]: 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 significantly improved AutoDock Vina’s screening performance from worse-than-random to better-than-random. For the Q variant, FRED exhibited the best enrichment when combined with the CNN re-scoring scheme, exhibiting the maximum value of EF 1% (ie, EF 1% = 31). pROC-Chemotype plots analysis revealed that these re-scoring combinations effectively retrieved diverse and high-affinity actives at early enrichment.Conclusion: The findings demonstrate that re-scoring with CNN-Score consistently augments the SBVS performance and enriches diverse and high-affinity binders for both PfDHFR variants, offering important endorsements for improving malaria drug discovery, especially against the highly resistant Q variant. Keywords: DEKOIS 2.0, MLSFs, docking, malaria