AI-Driven Discovery and BSL-4 Validation of Cross-Filovirus Ebola-Marburg Inhibitors and their Synergistic Combinations

Martin, H.-J.; Scotti, M. T.; Jain, S.; McMullan, L.; Chatterjee, P.; Melo-Filho, C.; Caza, M.; Tropsha, A.; Lin, H.; Flint, M.; Lee, E. M.; Lo, M. K.; Zakharov, A. V.; Muratov, E.

doi:10.64898/2026.07.09.737586

bioRxiv · 10.64898/2026.07.09.737586

AI-Driven Discovery and BSL-4 Validation of Cross-Filovirus Ebola-Marburg Inhibitors and their Synergistic Combinations

Martin, H.-J.·Scotti, M. T.·Jain, S.·McMullan, L.·Chatterjee, P.·Melo-Filho, C.·Caza, M.·Tropsha, A.·Lin, H.·Flint, M.·Lee, E. M.·Lo, M. K.·Zakharov, A. V.·Muratov, E.

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Abstract

Filovirus outbreaks caused by Ebola virus (EBOV) and Marburg virus (MARV), pose severe global health threats characterized by high rates of fatal hemorrhagic fever. While species-specific vaccines and therapeutic monoclonal antibodies are approved for Zaire ebolavirus, broadly-active therapeutics remain unavailable, leaving populations vulnerable to MARV and other pathogenic Ebola species, such as Bundibugyo (BDBV) and Sudan (SUDV) ebolaviruses. Here we report a computationally guided, infectious virus validated screening platform for the rapid discovery of broad-spectrum filovirus antivirals. By leveraging quantitative structure-activity relationship (QSAR) models, we screened 142,382 compounds in silico to prioritize 125 high-potential candidates. Subsequent dose-response and viability profiling identified 23 compounds exhibiting potent, low-micromolar pan-filovirus activity and favorable cytotoxicity profiles. Molecular docking indicates these compounds target conserved structural and functional domains--primarily the VP35 and L proteins--which may disrupt essential viral replication and immune antagonism. Furthermore, systematic combinatorial screening revealed three highly synergistic compound pairs, notably NCGC00113249-01 and NCGC00118008-01, demonstrating robust cross-species efficacy. By targeting conserved vulnerabilities across the filovirus family, this integrated in silico and in vitro pipeline provides a scalable framework to rapidly nominate and optimize synergistic therapeutic regimens against both endemic and emerging viral threats including BDBV. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/737586v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@531939org.highwire.dtl.DTLVardef@19d1454org.highwire.dtl.DTLVardef@1a1c9e3org.highwire.dtl.DTLVardef@cd861b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Martin, H.-J., Scotti, M. T., Jain, S., McMullan, L., Chatterjee, P., Melo-Filho, C., Caza, M., Tropsha, A., Lin, H., Flint, M., Lee, E. M., Lo, M. K., Zakharov, A. V., Muratov, E.. 2026-07-10. AI-Driven Discovery and BSL-4 Validation of Cross-Filovirus Ebola-Marburg Inhibitors and their Synergistic Combinations. https://doi.org/10.64898/2026.07.09.737586

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