By learning to transform AF3's internal representations, ConforNets can reliably generate multiple protein conformations and transfer conformational changes between proteins—solving a major limitation of structure prediction models that typically predict only one dominant state.
ConforNets is a method for controlling protein conformations in AlphaFold3 by applying learnable transformations to latent representations. Rather than perturbing inputs or using ad hoc tricks, it modulates the internal representations that AF3 uses to predict protein structures, enabling both discovery of alternate conformations and transfer of conformational changes across related proteins.