All Competitors

A generative protein-dynamics model that predicts short-horizon MD trajectories using rectified flow matching in velocity space over residue frames and torsions.

A residual VQ-VAE tokenizer that learns a discrete alphabet of protein conformational ensembles from molecular dynamics data, usable as a frozen representation layer for downstream tasks.

A protein language model that aligns ESM sequence embeddings with molecular-dynamics trajectory embeddings via contrastive learning for zero-shot mutation-effect prediction.

A probabilistic autoregressive model for protein molecular-dynamics trajectories that generates flexible-length paths frame-by-frame with an anti-drifting sampling strategy.

A physically grounded diffusion model that generates continuous-time, all-atom biomolecular dynamics trajectories at a fraction of the cost of molecular dynamics simulation.

Evolution-guided diffusion model that generates temporal protein folding pathways, from unfolded chain to native state, rather than static structures.

Generative deep learning model from Microsoft Research that emulates protein equilibrium ensembles at 100,000x the speed of molecular dynamics simulation.

Protein language models trained on biophysical dynamics from MD simulations and normal-mode analysis; ESMDance fine-tunes ESM2 for strong zero-shot mutation-effect prediction.