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.
Show 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@1251baorg.highwire.dtl.DTLVardef@b3a2feorg.highwire.dtl.DTLVardef@191d314org.highwire.dtl.DTLVardef@b8f710_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 14 journals account for 50% of the predicted probability mass.