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 generative foundation model for biomolecular dynamics that produces atom-level MD-style trajectories for protein monomers and protein-ligand complexes.

A polarizable electrostatic machine-learning interatomic potential extending MACE with long-range induction, trained on 100M OMol25 DFT calculations.

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

A universal all-atom machine-learning force field foundation model with ab initio-level accuracy on solvated biomolecular systems up to ~1,500 atoms.

A LoRA adapter on ProstT5 that predicts per-residue probability distributions over Foldseek 3Di tokens, capturing sequence-encoded conformational flexibility from MD trajectories.

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