Evaluating and Improving In Silico Drug Candidate Prediction for Preventing Toxoplasmic Encephalitis

Overview

The parasite Toxoplasma gondii infects up to 1/3 of the global population (Habtye et al., 2023). Immunocompromised individuals face a high risk of parasite reactivation, which can cause severe neurological effects (Wang et al., 2017). The development of medication to prevent these devastating effects from occurring represents a pressing global health issue. Artificial intelligence (AI) can help speed up drug discovery processes while cutting down on costs. However, machine learning models often act as black boxes and lack transparency (Patel & Shah, 2022). In the context of drug discovery, understanding the "why" behind a model's predictions is as crucial as the prediction itself. My project sought to integrate explainable AI tools to identify key chemical features and substructures that influence drug screening model predictions, helping establish biological interpretability. This research seeks to improve patient outcomes for vulnerable populations by identifying promising compounds to prevent Toxoplasma gondii-induced neurological effects from occurring.

Awards (2)

• Silver Medal
• Selected for CWSF 2026

Competition history

• CWSF 2026 Health & Wellness

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