ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation
Authors: Bharath Raghavan¹, David M. Rogers¹
Affiliations:
¹ National Center for Computational Sciences, Oak Ridge National Laboratory
Introduction
ProtNHF is a generative model for protein sequences that enables continuous, controllable design without retraining. It leverages neural Hamiltonian flows with a Transformer-based energy function to map a latent Gaussian to protein embeddings. Sampling-time bias functions allow steering properties like amino acid composition or net charge smoothly and predictably. Generated sequences achieve high quality as measured by ESM-2 pseudo-perplexity and AlphaFold2 pLDDT scores. ProtNHF provides a flexible, physically interpretable framework for programmable protein sequence generation.
The training was performed using Pytorch DDP on 64*8 GPUs, with a batch size per GPU of 30. The training was performed for 650 epochs. The optimizer and LR scheduler parameter are given below:
