Holli‐Joi Martin

This paper introduces a contrastive graph neural network to discover novel larvicidal compounds against Aedes aegypti, identifying LC-79 as a potent and selective candidate.

This paper identifies broad-spectrum filovirus inhibitors using computational screening and validates them against multiple viral species, finding synergistic compound pairs effective against Ebola and Marburg viruses.

The paper introduces a semantic-aware graph neural network framework for drug discovery, identifying LC-61 as a potent antileishmanial compound with nanomolar activity.

This study identifies conserved binding sites in filovirus proteins to guide the development of broad-spectrum antivirals, finding high conservation in L and VP40 proteins and validating existing drugs like Remdesivir.

The paper presents a framework that predicts endocrine disruptors using a multimodal cross-attentive graph model, achieving high accuracy and providing mechanistic explanations.

The paper uses machine learning to identify novel HDAC6/AKT2 inhibitors for myeloid cancer treatment, finding compounds with potent anticancer activity.

The paper curates a database of helicase inhibitors, Heli-SMACC, and tests selected compounds against SARS-CoV-2 NSP13 ATPase, finding twelve inhibitors with dose-response activity.

The paper curates a database of antiviral compounds tested against 13 emerging viruses, identifies potential broad-spectrum antivirals, and highlights the need for coordinated research efforts.