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Pre-trained and fine-tuned checkpoints for MapPFN: Learning Causal Perturbation Maps in Context (Sextro et al., 2026).

A Data-Driven Router architecture for ADMET prediction.

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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…

JAX/Equinox parameters for Promera, a dual-purpose biomolecular generative model for structure prediction and binder design. These weights were converted module-by-module from the official PyTorch checkpoint bjing-mit/promera (promera_2606.ckpt) and validated numerically against it (per-module…

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.

This repository contains the model weights for Zatom-1, a general-purpose model that unifies generative modeling and representation learning of 3D molecules and materials. Introduced in Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials, Zatom-1 is a Transformer trained…

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.