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Nesso-1 is a fast, structure-based protein–ligand binding-affinity model. Given a protein sequence and a ligand (SMILES / CCD code / SDF), it predicts a binding affinity scalar along with a binder/non-binder score.

This is the first release of the Cherimoya Accessibility aTlas (CATv1): a collection of over 7,500 Cherimoya models trained on DNase-seq and ATAC-seq experiments from the ENCODE Project. Cherimoya models are state-of-the-art predictors of local chromatin accessibility, mapping a DNA sequence to a…

DISCO (DIffusion for Sequence-structure CO-design) is a multimodal generative model that simultaneously co-designs protein sequences and 3D structures, conditioned on and co-folded with arbitrary biomolecules — including small-molecule ligands, DNA, and RNA.

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

A PyTorch port of AlphaGenome, the DNA sequence model from Google DeepMind that predicts hundreds of genomic tracks at single base-pair resolution from sequences up to 1M bp.

The complete neuron-to-neuron connectivity matrix extracted from a nanometer-resolution reconstruction of human temporal cortex (H01 dataset, Google/Harvard/Lichtman Lab).

JAMUN is a novel approach for generating conformational ensembles of protein structures, presented in the paper JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles.