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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 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

Trainable PyTorch reproduction of AlphaFold 3

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

Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research

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

protein design framework enabling DNA-encoding of chemistry for programmable

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

Protein structure prediction

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

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

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

protein language models demonstrating versatile generative and predictive capabilities for protein

protein language model family, including PMC-15B supporting sequence- and structure

protein structure design with multi-motif scaffolding capabilities, achieving state