All Competitors

Generative model that emulates protein equilibrium ensembles, sampling cryptic pockets and unfolded states far faster than molecular dynamics.

Latent diffusion model for controllable all-atom protein generation that co-designs sequence and structure while training on sequences alone.

Mixture-of-experts protein language model scaling to 16 billion parameters, applied to variant effect prediction and de novo protein design.

RNA foundation model with 1.6 billion parameters, pretrained on 42 million non-coding RNA sequences for structure prediction and RNA sequence design.

Open-source structure prediction model for proteins, nucleic acids, and small molecules, trained on public data to AlphaFold3-level accuracy.

End-to-end RNA 3D structure prediction from sequence alone, coupling the RNA-FM language model with an Invariant Point Attention structure module.

Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.

Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.

Open-source reproduction of AlphaFold 3 that predicts structures of proteins, DNA, RNA, and small-molecule ligands, including their mixed complexes.

End-to-end framework predicting protein structure and mutational fitness from a single sequence, with five-fold faster inference than ESMFold.

Lightweight AlphaFlow variant that fine-tunes only AlphaFold's structure module, keeping the Evoformer frozen to cut conformational sampling cost.

Multimodal generative protein language model reasoning jointly over protein sequence, structure, and function, trained at 98B parameters.

Multi-task protein framework recasting function, binding site, and structure prediction as autoregressive next-token prediction over ESM2 embeddings.

Trainable, open-source reimplementation of AlphaFold2 for protein structure prediction that matches its accuracy and runs 3-5x faster.

Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.

Diffusion-based structure prediction model for biomolecular complexes, spanning proteins with DNA, RNA, small molecules, ions, and modified residues.

RNA language model that builds base-pairing constraints into self-attention, pretrained on 20.4 million sequences for structure and function tasks.

Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.

RNA language model with 650M parameters pretrained on 36 million non-coding RNA sequences, generalizing structure prediction to unseen RNA families.

RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.

RNA secondary structure prediction from a single sequence, without MSAs, using an axial-attention transformer trained with strict homology controls.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

Multimodal protein pre-training framework jointly learning sequence, 3D structure, and surface representations via implicit neural representations.

RNA language model trained on multiple sequence alignments of Rfam families, predicting secondary structure and solvent accessibility from homology.