Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time

Daniel D. Richman (Computer Science Department, Stanford University) · Jessica Karaguesian (Stanford University) · Carl-Mikael Suomivuori (Yale University) · Ron Dror (Stanford University)
biomoleculesboltzmann distributionclassifier guidanceconformational distributionsconformational variabilitycryptic pocket flexibilitydiffusion modelsdomain motionfree energy estimationinference-time algorithmmodel pretrainingscalabilitystructural changestransporter cyclingunphysical states

The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or conformations. Researchers have endeavored for decades to develop computational methods to predict the distribution of conformations, which is far harder to determine experimentally than a static folded structure. We present ConforMix, an inference-time algorithm that enhances sampling of conformational distributions using a combination of classifier guidance, filtering, and free energy estimation. Our approach upgrades diffusion models