Georgios Chrysochoou is a computational researcher specializing in the development and validation of in silico models for chemical safety assessment. While his doctoral research focused on Computer-Aided Drug Discovery (CADD)—including the development of R-language tools for ethnopharmacological lead identification—his current work centers on Computational Toxicology. His research is driven by the mission to reduce reliance on animal testing by increasing the predictive power and regulatory acceptance of chemical risk assessments. This is achieved through the integration of Machine Learning (ML), Quantitative Structure-Activity Relationships ((Q)SARs), and quantitative Adverse Outcome Pathways (qAOPs). A key pillar of his recent work is the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles in computational toxicology to ensure model transparency and utility. Georgios has contributed to international frameworks for qAOP validity (SOT 2025) and has published extensively on improving QSAR predictivity and Weight-of-Evidence (WoE) approaches in journals such as ALTEX and Computational Toxicology. He is a committed advocate for Open Science and a contributor to the QsarDB repository.