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Automates and standardizes ligand preparation for AutoDock Vina.

Multimodal LLM-based AI agent enabling deep research in spatial transcriptomics, automating analysis and interpretation of spatial gene expression data (Harvard LiuLab, bioRxiv 2025)

DeepMind's neural network for ab-initio quantum chemistry, directly solving the many-electron Schrödinger equation via variational Monte Carlo with antisymmetric wavefunctions, extended to excited states (Phys. Rev. Research 2020, Science 2024)

Deployable biomedical deep-research agent blueprint combining on-prem multimodal RAG, report generation, human-in-the-loop editing, and virtual screening with MolMIM and DiffDock for drug discovery workflows (2025)

General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)

Open-source self-supervised vision foundation model for Earth observation by Clay Foundation (non-profit), a Masked Autoencoder ViT pretrained on multimodal satellite imagery (Sentinel-1/2, Landsat 8-9, NAIP, MODIS, LINZ DEM) with location/time embeddings, supporting classification, segmentation, change detection, similarity search, and few-shot downstream geospatial tasks (Apache 2.0, v1.5 2024-2025)

Fast, all-atom SE(3)-equivariant diffusion model for protein design achieving state-of-the-art performance on unconditional generation, motif scaffolding, and binder design while retaining the computational efficiency of equivariant architectures (bioRxiv 2026)

2D interactive visualization in Jupyter.

General-purpose RNA language model with 650M parameters pretrained on 36M non-coding RNA sequences, achieving strong generalization on structure prediction tasks including secondary structure prediction, splice-site prediction, mean ribosome loading, and ncRNA classification (lbcb-sci, 165+ stars, Apache-2.0)

Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization

Protein structure prediction

FutureHouse's end-to-end scientific discovery multi-agent system orchestrating literature search (Crow/Falcon) and data analysis (Finch) agents, first AI-generated drug discovery identifying ripasudil as novel dry AMD therapeutic (2025)

Descriptor library containing a variety of fingerprinting techniques, including the Smooth Overlap of Atomic Positions (SOAP).

Fully autonomous medical image segmentation research system that generates complete manuscripts end-to-end from datasets with zero human intervention, beating strongest baselines on 24 of 31 datasets and achieving T1-T2 tier manuscript quality in double-blind evaluations (USTC & Shanghai AI Lab, 2026)

First large vision-language assistant for gigapixel whole-slide pathology image understanding, released with the SlideInstruction dataset and SlideBench benchmark (uni-medical, Apache 2.0, 2025)

Allen Institute for AI's global geospatial foundation model for satellite imagery analysis, enabling large-scale mapping of buildings, wind turbines, trees, and land cover from Sentinel-2 data with open-source weights and inference tools (2024)

Toolkit for linearizing academic PDFs into LLM-ready text with high accuracy and structure preservation, optimized for scientific literature extraction

End-to-end semi-automated scientific discovery system that designs, iterates, and analyzes code-based experiments via LLM-as-a-mutator over scientific articles and code examples; auto-creates, runs, and debugs experiment code in containers and writes meta-analysis reports (339+ stars, Apache 2.0)

Automated code generation from machine learning research papers into runnable implementations (4.5K+ stars, 2025)

Target-aware peptide design framework that treats receptor sequence and structure as context via multimodal adapter tuning of protein language models (ESMC + ProteinMPNN features), with reinforcement-learning-based 3D dynamic feedback (ESMFold structure evaluation) to suppress unrealistic peptide conformations; ships with a systematic assessment pipeline covering peptide-target affinity, structure quality, physicochemical properties, diversity, and novelty (142+ stars, Apache 2.0, 2026)

Bi-directional DNA language model based on the Mamba state space architecture, enabling efficient long-range genomic sequence modeling with linear-time complexity and built-in reverse-complement equivariance; achieves strong performance on chromatin accessibility, enhancer, and promoter prediction benchmarks (Stanford & UC Berkeley, 500+ stars)

This tutorial aims to illustrate the process of analyzing a membrane molecular dynamics (MD) simulation, step by step, using the BioExcel Building Blocks (biobb)

This tutorial aims to illustrate the process of checking a molecular structure before using it as an input for a Molecular Dynamics simulation, step by step, using the BioExcel Building Blocks (biobb).

This BioExcel Building Blocks library (BioBB) workflow provides a pipeline to setup DNA structures for the Ascona B-DNA Consortium (ABC) members. It follows the work started with the NAFlex tool to offer a single, reproducible pipeline for structure preparation, ensuring reproducibility and coherence between all the members of the consortium.

This tutorial aims to illustrate the process of generating protein conformational ensembles from 3D structures using Coarse-Grained tools from the FlexServ server and analysing its molecular flexibility

This tutorial aims to illustrate the process of computing a conformational transition between two known structural conformations of a protein, step by step, using the BioExcel Building Blocks (biobb).

This tutorial aims to illustrate the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb).

This tutorial aims to illustrate the process of computing classical molecular interaction potentials from protein structures step by step, using the BioExcel Building Blocks library (biobb)

This tutorial aims to illustrate how to compute a fast-growth mutation free energy calculation, step by step, using the BioExcel Building Blocks library (biobb). The particular example used is the Staphylococcal nuclease protein (PDB code 1STN), a small, minimal protein, appropriate for a short tutorial.

The initial focus of the GS1 Web Vocabulary is consumer-facing properties for clothing, shoes, food beverage/tobacco and properties common to all products. [from homepage]

Foundation model for joint segmentation, detection, and recognition of biomedical objects across nine imaging modalities, with v2 introducing BoltzFormer architecture for end-to-end 3D inference (Microsoft, Nature Methods 2025)

DNA sequence analysis

DeepMind's Olympiad-level geometry theorem prover combining neural language model with symbolic deduction engine, AlphaGeometry2 solves 84% of IMO geometry problems (42/50) at gold-medalist level (Nature 2024)

Standard data-centric AI package for data quality and machine learning, automatically detecting label errors, outliers, and dataset issues to improve scientific dataset reliability and model performance (11K+ stars, MIT License)

Efficient foundation model and benchmark for multi-species genome understanding with context-aware nucleotide representations, improving upon DNABERT for diverse genomic task transfer learning (UIUC MAGICS Lab, 484+ stars)

Fast, modular, and accurate de novo design of protein binders based on the Protenix foundation model, achieving 17-82% nanomolar hit rates across diverse targets with 2-6× improvement over prior methods like AlphaProteo and RFdiffusion (229+ stars, Apache 2.0)

Trainable, memory-efficient PyTorch reproduction and retraining of AlphaFold2 providing new insights into its learning dynamics and out-of-distribution generalization; widely used as the open-source AlphaFold2 backbone underpinning many downstream protein structure prediction and design pipelines (Columbia AlQuraishi Lab & OpenFold Consortium, Nature Methods 2024)

Full spaCy pipeline and models for scientific/biomedical documents, enabling named entity recognition, abbreviation resolution, and UMLS linking for scientific literature mining (1.9K+ stars, Apache 2.0)

Autonomous multi-agent research loop for model architecture discovery that ran 1,773 experiments over 20,000 GPU hours and produced 106 state-of-the-art linear-attention architectures, surpassing human-designed baselines including Mamba2 and DeltaNet (1.1K+ stars, Apache 2.0)

100M-parameter foundation model pretrained on 50M+ human single-cell transcriptomes covering ~20,000 genes, achieving SOTA on gene expression enhancement, drug response and perturbation prediction (Nature Methods 2024)

Teaching Large Language Models the Language of Biology through single-cell transcriptomics (ICML 2024)

First versatile medical reasoning agent for chest X-ray interpretation, dynamically integrating state-of-the-art CXR analysis tools and multimodal LLMs into a unified framework; introduces ChestAgentBench with 2,500 complex medical queries across 7 categories (bowang-lab, 1.1K+ stars)

A library for computational chemistry (DFT) for input file generation, data extraction, method screening and analysis.

Generalist foundation model and database for open-world medical image segmentation, enabling universal segmentation of diverse anatomical structures and pathologies with zero-shot generalization to unseen tasks and modalities (Nature Biomedical Engineering 2025)

Automated and rigorous experiments using AI agents for scientific discovery

Open-ended self-improving agent that iteratively rewrites its own codebase and empirically validates each mutation on coding benchmarks (SWE-bench, Polyglot), demonstrating open-ended evolution where agents improve their ability to improve themselves, diverging into a population of diverse specialists (arXiv 2505.22954, 2.3K+ stars, Apache 2.0, 2025)

Retrieval-augmented LM synthesizing scientific literature from 45M papers with human-expert-level citation accuracy, outperforming GPT-4o by 5% on ScholarQABench (Nature 2026, UW & Ai2)

Deep Graph Library for scalable deep learning on graphs, powering molecular modeling, materials discovery, protein interaction networks, and scientific knowledge graph learning across PyTorch, TensorFlow, and MXNet backends (14K+ stars)

Family of diffusion protein language models demonstrating versatile generative and predictive capabilities for protein sequences and structures, including multimodal co-generation, conditional folding, inverse folding, motif scaffolding, and representation learning, with open pretrained weights and training scripts (327+ stars, ICML 2024, ICLR 2025, ICML 2025 Spotlight)

Public release of Profluent's ProGen3 protein language model family, including PMC-15B supporting sequence- and structure-conditioned generation for protein design, zero-shot fitness prediction, and antibody engineering with state-of-the-art performance on fitness and docking benchmarks (114+ stars, Apache 2.0)