Computational Biology
Showing 36 of 67 projects
A collection of 134 ready-to-use Agent Skills for scientific research, covering genomics, drug discovery, clinical analysis, and more.
Open source implementation of AlphaFold 2, a deep learning system for highly accurate protein structure prediction.
AlphaFold 3 is an AI model that predicts the 3D structures of proteins and their interactions with other biomolecules like DNA, RNA, and ligands.
A free, self-taught curriculum for learning bioinformatics using online courses from top universities.
An open-source Python library for applying deep learning to drug discovery, materials science, quantum chemistry, and biology.
A collection of transformer protein language models for predicting structure, function, and designing proteins from sequences.
A family of open-source deep learning models for accurate biomolecular interaction and binding affinity prediction, rivaling AlphaFold3 and physics-based methods.
A trainable, memory-efficient PyTorch reproduction of AlphaFold 2 for protein structure prediction.
An open-source diffusion model for generating and designing protein structures, including binders, symmetric oligomers, and motif-scaffolded proteins.
A multimodal protein language model for generative protein design and engineering by jointly reasoning over sequence, structure, and function.
A deep learning system for accurate protein structure and interaction prediction using a three-track neural network.
A fast RNA-seq aligner for mapping spliced transcript sequences to a reference genome.
A curated list of deep learning implementations and resources for biological research, with a focus on genomics.
A multi-modal foundation model for state-of-the-art molecular structure prediction of proteins, small molecules, DNA, RNA, and glycosylations.
A deep learning model for protein sequence design that generates amino acid sequences for given protein backbones.
A transformer-based foundation model pretrained on millions of single-cell profiles for generative AI tasks in single-cell multi-omics.
A state-of-the-art diffusion model for predicting how small molecules (ligands) bind to proteins.
State-of-the-art pre-trained transformer language models for protein sequences, enabling tasks like structure prediction and function annotation.
WebGL-accelerated JavaScript library for interactive molecular visualization in web applications.
A curated directory of academic institutions and principal investigators in computational neuroscience worldwide.
A pre-trained BERT model designed for DNA sequence analysis, enabling genome understanding tasks like classification and motif discovery.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A benchmark for evaluating protein language models through five biologically relevant semi-supervised learning tasks.
A Go package providing fast, reproducible computational tools for synthetic biology and organism engineering.
A Dash component library for creating interactive and customizable network visualizations in Python and R, powered by Cytoscape.js.
A diffusion framework for controllable protein sequence and evolutionary alignment generation using discrete diffusion models.
Fast, sensitive, and accurate integration of single-cell RNA-seq data across multiple datasets, batches, or experimental conditions.
High-resolution de novo protein structure prediction from amino acid sequences using deep learning.
A curated reading list of foundational genomics papers for computational biologists and statistical genomics students.
A scalable Python toolkit for RNA velocity analysis in single cells using dynamical modeling.
An R package to infer gene regulatory networks and identify cell types from single-cell RNA-seq data.
A deep learning framework for integrating single-cell multi-omics data using graph-linked unified embeddings.
A comprehensive benchmark suite for evaluating protein fitness prediction models using deep mutational scanning and clinical variant data.
A Python package for benchmarking and evaluating single-cell genomics data integration methods.
A 100M-parameter foundation model for single-cell transcriptomics, enabling gene expression enhancement, drug response prediction, and perturbation analysis.
A factor analysis framework for unsupervised integration of multi-omics datasets.
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