Biology subjects

Martin, H.-J.

Publications and source records attributed to Martin, H.-J..

7 recordsLinked to original sources

bioRxiv2026

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

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

microbiology↗

bioRxiv2026

Instance-Wise Contrastive Graph Neural Network Enables the Discovery of Novel Aedes aegypti Larvicidal Compounds

Aedes aegypti remains a major arboviral vector, making larval control a critical strategy to reduce mosquito populations. However, resistance to commercial larvicides has reduced the long-term effectiveness of current interventions, reinforcing the need for new compounds with improved potency and selectivity. Here, we present an instance-wise contrastive graph neural network (GNN) framework to accelerate the discovery of novel larvicidal compounds. The model was trained on a curated dataset of 556 organic compounds organized into LC50-derived multitask classification thresholds and integrated Transformer-inspired graph learning with whole-molecule and fragment-level contrastive regularization. This model achieved strong held-out performance, with global AUC = 0.95 {+/-} 0.01, PR-AUC = 0.93 {+/-} 0.01, and MCC = 0.77 {+/-} 0.03, outperforming conventional machine learning and graph-based baselines. Predictive uncertainty analysis and counterfactual maps further supported the interpretation of threshold-sensitive predictions and substructural contribution patterns. The model was applied to screen 1.3 million compounds, resulting in 10 candidates for experimental validation. Three compounds showed measurable larvicidal activity against A. aegypti larvae. Among them, LC-79 emerged as the most promising hit, with 2-day and 5-day LC50 values of 0.24 {micro}g/mL (0.66 {micro}M) and 0.05 {micro}g/mL (0.13 {micro}M), respectively, an IE50 of 0.06 {micro}g/mL (0.16 {micro}M), and rapid larval mortality (LT50 = 1.10 days at 1 {micro}g/mL). LC-79 also showed no measurable acute toxicity to Daphnia magna at the highest tested concentration [EC50-48h >43 {micro}g/mL (>119 {micro}M)], resulting in selectivity indices >180 and >860 relative to its 2-day and 5-day LC50 values. Overall, this study demonstrates that contrastive graph learning can move beyond retrospective larvicide modeling to experimentally validated hit discovery, identifying LC-79 as a potent and preliminarily selective acylthiourea larvicide candidate for further mechanism-of-action, resistance, and semi-field evaluation.

bioinformatics↗

bioRxiv2025

Semantic-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Leishmania infantum Compound

Visceral leishmaniasis caused by Leishmania infantum remains a lethal disease with few therapeutic options, necessitating innovative computational methods approaches to accelerate drug discovery. Here, we present a semantic-aware graph neural network (GNN) framework that features holistic mechanisms to capture long-range molecular interactions and the chemical semantics of antileishmanial compounds. Across two classificatory antileishmanial datasets, our holistic GNNs demonstrated significant improvements in predictive performance, with area under the receiver operating characteristic curve (AUROC) increases of 2.2-29.2% on the unbalanced dataset (1 {micro}M threshold) and 3.4-22.5% on the balanced dataset (10 {micro}M threshold) compared to default GNNs. Subsequently, the framework was applied to screen a library of approximately 1.3 million compounds, pinpointing LC-61 as a potent antileishmanial agent with nanomolar activity against intracellular L. infantum (IC50 = 0.076 {micro}M) and minimal cytotoxicity to macrophages (THP-1 CC50 = 157 {micro}M). A comprehensive in vitro ADME profiling revealed that LC-61 combines high solubility at both acidic and physiological pH (>28 {micro}g/mL), balanced lipophilicity (eLogD = 4.07), and favorable passive permeability (PAMPA = 4.86 x 10-6 cm/s), while exhibiting lower microsomal stability. Overall, our semantic-aware GNN framework effectively accelerated the discovery of LC-61, a novel and biologically validated hit suitable for hit-to-lead optimization.

bioinformatics↗

bioRxiv2025

Conserved Filovirus Proteins as Targets of Broad-Spectrum Antivirals

Filoviruses are enveloped, non-segmented, negative-strand RNA viruses belonging to the Filoviridae family, which includes five genera: Ebolavirus, Marburgvirus, Cuevavirus, Striavirus, and Thamnovirus. Members of this family cause severe and, often, fatal hemorrhagic fevers in humans and non-human primates, with high mortality rates. To date, only two filoviruses, Ebola virus (EBOV) and Marburg virus (MARV), are known to infect humans and are listed as priority pathogens by the World Health Organization due to their potential for re-emergence and the current lack of effective vaccines and antiviral treatments. In this study, we identify and characterize conserved binding sites within key filoviral proteins to support the development of broad-spectrum, direct-acting antiviral agents. We validated the significance of these conserved regions for drug discovery using existing experimental data. Our analysis revealed notably high sequence similarity among proteins from filoviruses capable of infecting humans (EBOV, TAFV, BDBV, SUDV, MARV, and RAVV) compared to those from non-zoonotic species, with the highest conservation observed in the L and VP40 proteins--both critical for viral genome transcription and replication. Furthermore, we compiled and analyzed available experimental data on known antiviral compounds targeting these proteins, identifying several agents with cross-filovirus activity, including Galidesivir, Remdesivir, and Favipiravir. The integrated approach described here--combining sequence and structural conservation analysis with chemical structure and antiviral activity data--demonstrates a strategy that could be extended to the development of broad-spectrum therapeutics across multiple viral families. HIGHLIGHTSO_LIConserved filovirus sites targeted for broad-spectrum antivirals. C_LIO_LIStructural modeling identifies key antiviral binding sites. C_LIO_LIViral internal proteins are crucial targets for inhibition. C_LIO_LIRemdesivir validates conserved polymerase as a druggable target. C_LIO_LIStudy highlights need for pan-filovirus drug screening C_LI TOC GRAPHIC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/678902v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@4fd25eorg.highwire.dtl.DTLVardef@7c4076org.highwire.dtl.DTLVardef@16c0961org.highwire.dtl.DTLVardef@1dbd8a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Multimodal Cross-Attentive Graph-Based Framework for Predicting In Vivo Endocrine Disruptors

Endocrine hazard assessment needs models that are accurate and mechanistically transparent. We present a multimodal cross-attentive graph framework that fuses molecular graphs with adverse-outcome-pathway (AOP)-anchored assay signals to predict organism-level outcomes in the OECD Hershberger and uterotrophic assays. In Tier-1, multitask GNNs learn ER/AR molecular-initiating and key events across 46 ToxCast/Tox21 assays. In Tier-2, a cross-attentive multimodal GNN integrates Tier-1 pathway signals with molecular graphs, achieving AUROC{square}={square}0.90 (Hershberger) and 0.96 (uterotrophic). External validation on literature compounds showed 84% concordance (Hershberger 15/18; uterotrophic 22/26). Bidirectional cross-attention links molecular substructures to pathway assays and vice-versa, while counterfactual perturbations rank assays and structural motifs most responsible for each decision. The framework couples high accuracy with assay-traceable explanations, supporting targeted testing within the Integrated Approaches.

pharmacology and toxicology↗

bioRxiv2024

Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment

Hematological cancer treatment with hybrid kinase/HDAC inhibitors is a novel strategy to overcome the challenge of acquired resistance to drugs. We collected IC50 datasets from the ChEMBL database for 13 cancer cell lines (72 h cytotoxicity, measured by MTT), known inhibitors for 38 kinases, and 10 HDACs isoforms, that we identified by target fishing and literature review. The data was subjected to rigorous biological and chemical curation leaving the final datasets ranging from 76 to 8173 compounds depending on the target. We generated Random Forest classification models, whereby 14 showed greater than 80% predictability after 5-fold external cross-validation. We screened 30 hybrid kinase/HDAC inhibitor analogs through each of these models. Fragment-contribution maps were constructed to aid the understanding of SARs and the optimization of these compounds as selective kinase/HDAC inhibitors for cancer treatment. Among the predicted compounds, 9 representative hybrids were synthesized and subjected to biological evaluation to validate the models. We observed high hit rates after biological testing for the following models: K562 (62.5%), MV4-11 (75.0%), MM1S (100%), NB-4 (62.5%), U937 (75.0), and HDAC6 (86.0%). This aided the identification of 6b and 6k as potent anticancer inhibitors with IC50 of 0.2-0.8 {micro}M in three cancer cell lines, linked to HDAC6 inhibition below 2 nM, and blockade of AKT2 phosphorylation at 2 M, validating the ability of our models to predict novel drug candidates. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/626092v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@76705dorg.highwire.dtl.DTLVardef@1cb1d32org.highwire.dtl.DTLVardef@1e9cbdforg.highwire.dtl.DTLVardef@47b819_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LINovel kinase/HDAC inhibitors for cancer treatment were found using machine learning C_LIO_LI61 QSAR models for hematological cancers and its targets were built and validated C_LIO_LIK562, MV4-11, MM1S, NB-4, U937, and HDAC6 models had hit rates above 62.5% in tests C_LIO_LI6b and 6k presented potent IC50 of 0.2-0.8 {micro}M in three cancer cell lines C_LIO_LI6b and 6k inhibited HDAC6 below 2 nM, and blockade of AKT2 phosphorylation at 2 M C_LI

cancer biology↗

bioRxiv2024

Heli-SMACC: Helicase-targeting SMAll Molecule Compound Collection

Helicases have emerged as promising targets for the development of antiviral drugs; however, the family remains largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all available helicases from the ChEMBL database. After thoroughly curating and enriching the data with relevant annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). The current version of Heli-SMACC contains 20,432 bioactivity entries for viral, human, and bacterial helicases. We have selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu. HighlightsO_LIWe created a curated Helicase-Targeting SMAll Molecule Compound Collection (Heli-SMACC). C_LIO_LIHeli-SMACC covers 29 human, viral, and bacterial helicases. C_LIO_LITwelve of thirty selected compounds demonstrated inhibitory activity in a SARS-CoV-2 NSP13 ATPase Assay. C_LIO_LIHeli-SMACC is freely available online at https://smacc.mml.unc.edu. C_LI TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/602122v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@8e098forg.highwire.dtl.DTLVardef@115cb2borg.highwire.dtl.DTLVardef@1cd9da3org.highwire.dtl.DTLVardef@2870a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗