Machine Learning for Simulations in Biology and Chemistry - The 2nd SIMBIOCHEM Workshop

Machine learning has achieved landmark progress in structure prediction, generative molecular design, learned potentials, and automated scientific workflows. Yet many models still treat molecular systems as static objects, or rely on physical representations that do not capture conformational ensembles, kinetics, allostery, rare events, thermodynamics, and experimental context. Physics-based simulation provides the mechanistic grounding needed to study these phenomena, but remains too costly for routine use at discovery scale, and a persistent gap remains between computational predictions and experimental reality. The 2nd SIMBIOCHEM Workshop addresses a pressing research challenge: how simulation, generative models, and agentic scientific tooling can be combined into reliable molecular AI systems. Its distinctive emphasis is on molecular simulation as a training signal, mechanistic constraint, and validation layer for models that reason over dynamics, uncertainty, and physical evidence. The workshop will bring together machine learning, computational chemistry, biophysics, life science, and materials science communities to discuss foundation models for molecular systems, simulation-derived post-training, learned force fields, differentiable and enhanced molecular dynamics, uncertainty-aware prediction, and agents that plan simulations, call MD or QM tools, evaluate outputs, and guide the next computational or experimental step.