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Fully open-source (Apache 2.0) biomolecular structure prediction reproducing AlphaFold3

Deep learning framework for molecular docking extending AutoDock Vina with

protein language models (300M/600M/6B) trained on billions of protein sequences

protein binder design with test-time optimization, SOTA on binder

protein design platform via Google Colab integrating AlphaFold2, RoseTTAFold, and ProteinMPNN

Neural network-based cryo-EM heterogeneous reconstruction, modeling continuous 3D

protein design using diffusion modeling, equivariant graph neural networks, and conditional

proteins, nucleic acids, small molecules, ions, and post-translational modifications

proteins, nucleic acids, and small molecules in drug discovery; ranked

protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting

protein design framework integrating Boltz, AlphaFold2, OpenFold3, ProteinMPNN, and ESM via JAX-based

protein sequence, structure, and biochemical properties to predict class-I immunogenicity

protein binder design pipeline using AlphaFold2 backpropagation, MPNN, and PyRosetta

Trainable PyTorch reproduction of AlphaFold 3

AlphaFold/ESMFold accessible implementation with AF3 JSON export, database updates

proteins, nucleic acids, and complexes, featuring a public web server

protein particles in micrographs at competitive precision with far fewer

protein equilibrium conformations 100,000× faster than MD simulations, predicting

protein sequence, structure, and function, trained on 2.78 billion proteins

protein folding model using only general-purpose transformer layers, scaled

protein structure search using a learned 3Di structural alphabet (VQ-VAE) that

proteins and regions from sequence, with GPU/CPU inference, trajectory export

Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research

proteins, nucleic acids, small molecules, and cyclic peptides with fine

proteins, small molecules, DNA, RNA, glycans) achieving SOTA across benchmarks

Cross-platform system optimizations for accelerating AlphaFold3 training with 1.73

Learning the language of protein-protein interactions

protein structure determination from electron density with high accuracy (3DEM/EMBL

proteins via backpropagation through compositional design constraints parameterized by structure

First fully open-source model achieving AlphaFold3-level accuracy with

protein-nucleic acid-small molecule-metal ion complexes, enabling accurate

protein design framework enabling DNA-encoding of chemistry for programmable

protein design achieving state-of-the-art performance on unconditional

Semi-flexible molecular diffusion model for structure-based drug design

Protein structure prediction

proteins, nucleic acids, ligands, ions, and post-translational modifications within

protein conformational ensembles, covering both experimental PDB states and molecular

protein language models (ESMC + ProteinMPNN features), with reinforcement-learning-based

protein language model using 3D structural vocabulary (Foldseek) for joint

protein language model translating between protein sequence and structure, finetuned

protein backbone generation, 37× faster than RFDiffusion with 0.972 designability

proteins with noncanonical amino acids, enabling AI-powered modeling of synthetic

protein sequence design over evolutionary-scale databases, supporting unconditional generation

protein binders based on the Protenix foundation model, achieving 17-82% nanomolar

protein-ligand complex structures with dynamic receptor conformational flexibility, enabling

protein structure prediction and design pipelines (Columbia AlQuraishi Lab & OpenFold

protein-ligand conformational ensemble prediction and docking, generating diverse solutions

protein language models via sparse autoencoders, enabling mechanistic understanding of PLM representations

protein-ligand complex structure prediction with a multi-scale deep

protein function by integrating structural and evolutionary constraints into protein