Scope

Free energy (FE) simulations are a powerful class of molecular simulation methods used to investigate a wide range of chemical and biological processes, including drug binding, protein folding, protein–protein interactions, allosteric regulation, gas adsorption and diffusion in metal–organic frameworks (MOFs), piezoelectric responses, and catalytic events. Improving the accuracy and efficiency of these simulations remains an active area of research, particularly with the growing integration of machine learning potentials (MLPs) and artificial intelligence (AI) to expand the scope and reliability of free energy methods. As computational resources and theoretical techniques continue to advance, so too does the potential impact of free energy simulations. However, these methods are often technically challenging to set up and execute.

This workshop will equip computational scientists with the knowledge and practical skills to perform robust, accurate, and efficient free energy simulations using the AMBER software suite. AMBER offers an advanced array of free energy simulation and analysis methods with high-throughput enabled by its efficient GPU-accelerated free energy simulation engine.  Through a combination of lectures and expert-guided hands-on tutorials, participants will learn best practices and explore the latest features in AMBER, including new enhanced sampling techniques, optimized alchemical transformation pathways, free energy surface and minimum free energy path methods.  AMBER further offers a diverse set of generalized hybrid quantum mechanical (QM), molecular mechanical (MM) and machine learning potential (MLP) force fields enabled by interoperable software infrastructure.  Free energy workflows provide a framework from which complex networks of simulations can be efficiently set up, executed and analyzed.  Emphasis will be placed on driving applications to enzyme design and drug discovery.