David Rouquie is leading the Toxicology Data Science team on the toxicology facility of Bayer Crop Science in Sophia Antipolis, France. His passion about science and research addressing societal needs kept him involved in innovative, collaborative and multidisciplinary research work. By training, he is biochemist and molecular biologist, continuously metamorphosing toward a hybrid profile at the interface between biological and computational sciences. He recently joined the scientific committee of ECETOC as Bayer representative.
Applications of NAMs for early derisking: opportunities and challenges
Advances in artificial intelligence (AI) and high-biological dense assays such as Cell Painting is revolutionizing the fields of small molecule discovery and toxicology. In this presentation, we explore the integration of AI with cell painting data—a morphological profiling method that captures cellular responses to chemical exposure—to enhance toxicity prediction and facilitate de novo compound design. By employing machine learning algorithms, we extract complex patterns from high-dimensional cell painting images, enabling more accurate identification of toxicity profiles and biological mechanisms triggered by chemical compounds. Furthermore, as proof of concept we apply AI-guided generative models to design novel compounds with desired biological properties. This approach not only accelerates the identification of safe and effective candidates but also reduces reliance on costly and time-intensive animal testing. Our results demonstrate that the synergy between AI and cell painting data holds significant promise for early derisking and chemical design, paving the way for more efficient and data-driven approaches in chemical safety assessment and small molecule discovery.