AI tools for biology, verified.

Illumina's unified cloud environment for genomic and multiomic analysis, bundling DRAGEN, BaseSpace, Emedgene, Connected Multiomics and curated data under one usage-based credit system.

Seqera's closed-loop discovery system: AI agents design computational experiments, run thousands of them as Nextflow pipelines, score the results and iterate. Offered as a partnership.

Text-to-structure all-atom protein design: generate monomers and binders from natural-language prompts.

1.2B DNA model that annotates protein-coding genes across eukaryotes; ships a 48K-assembly annotation database.

Illumina's successor to SpliceAI: predicts splice-site usage and full transcript isoforms from sequence.

Google DeepMind diffusion pipeline for de novo enzyme design via catalytic motif scaffolding.

Virtual-cell model using curated biological knowledge to predict perturbation responses in unseen contexts.

Changping Lab × Huawei NPU-native all-atom biomolecular model suite: TorchFold (antibody–antigen), TorchScore and TorchCraft (design); MIT weights + training data.

Unified framework for cell segmentation and cell-type annotation in high-resolution spatial transcriptomics such as Xenium and Visium HD.

NVIDIA's 3D vision-language model for chest and abdominal CT: structured reports, visual QA and step-by-step reasoning.

Self-supervised encoder foundation model trained on 1.63B MS/MS spectra; transferable embeddings for de novo and database-independent identification.

Metagenome binning plus microbial SNP calling and metagenome-wide association with host traits in one framework.

First predictive model from the UK OpenBind consortium, trained on its experimental protein–ligand dataset.

Arc Institute's perturbation benchmark: predict CRISPRi knockdown responses in six cell lines never seen perturbed, from their unperturbed state alone.

Recursion's Apache-2.0 co-folding model for fast protein–ligand binding-affinity prediction.

Apache-licensed all-atom co-folding foundation model for structure prediction, design and optimization in drug discovery (preview).

Anthropic's AI workbench for scientists: coordinating agent with 60+ skills and connectors for genomics, single-cell, proteomics and more, plus a reviewer agent (beta).

Biohub's protein 'world model': ESMC language model (~2.8B sequences), ESMFold2 complex structure prediction/design, and ESM Atlas (6.8B sequences).

Hugging Face's family of open generative DNA language models (500M, 3B, 8B) with 6-mer tokens and up to ~786 kbp context; zero-shot variant-effect scoring and sequence generation.

EPFL's fully open medical LLMs (open weights, training data and recipe) built on Apertus, OLMo-2, EuroLLM and Gemma-3 bases.

AI partner inside the Seqera platform for building, launching and debugging Nextflow bioinformatics pipelines.

AWS antibody-design platform: 40+ hosted biological AI models, agents and no-code pipelines, with wet-lab ordering through partner CROs and results fed back for model refinement.

Diffusion model that co-designs protein sequence and structure conditioned on ligands, DNA or RNA.

1.4B-parameter long-context generative RNA foundation model trained on 114M full-length RNAs; supports RNA engineering and guide-RNA design.

1.1B-parameter open-weight histopathology foundation model trained only on public data.

~8M-cell in vivo CRISPR perturbation atlas of mouse brain (single-nucleus), downloadable on Hugging Face in Parquet/AnnData/Zarr.

Browser-accessible autonomous biology lab on Ginkgo's automation infrastructure, with an AI agent that checks protocol feasibility and pricing.

Structural variant caller with haplotype-resolved local assembly that works on PacBio HiFi, ONT, hybrid and short-read data.

David Liu lab's mechanistic ML model that predicts prime-editing efficiency and outcomes for PE2, PE3 and twinPE, with a web server.

Isomorphic Labs Drug Design Engine: proprietary system predicting protein–ligand and antibody–antigen structures, binding affinity and cryptic pockets beyond AlphaFold 3.

Phylo's 'integrated biology environment': a workspace where biologists orchestrate AI agents across 300+ databases, tools and software (preview).

Profluent's protein language model that predicts a Cas enzyme's PAM specificity from its sequence and guides PAM-engineering of Cas9 variants.

AI scientist from BIO with planning, literature, data-analysis and novelty-detection subagents; hosted at chat.bio.xyz (the earlier open BioAgents framework is archived).

NVIDIA RNA 3D folding model built with hosts and winners of the Stanford RNA 3D Folding Kaggle competition.

Arc Institute model trained on 149M human cells using tabular attention for in-context learning; used to generate the Perturb Sapiens atlas (28 tissues, 201 perturbations).

Pathology foundation model family built for clinical deployment (Aignostics and collaborators).

Multi-species DNA foundation model with 1 Mb single-base context; predicts ~16k functional tracks, annotates genomes, and designs enhancers.

Microsoft and Providence model that turns routine H&E slides into virtual multiplex immunofluorescence maps of 21 proteins.

All-atom generative world model for designing proteins, ligands, nucleic acids and cyclic peptides that bind a given target.

Open generative model for designing protein and peptide binders against diverse targets.

Edison Scientific's AI scientist: long autonomous runs that read ~1,500 papers and write tens of thousands of lines of analysis code per run.

Open-source AlphaFold3-style model predicting structures of proteins, nucleic acids and small-molecule complexes (preview release).

Perturbation-trained single-cell foundation models up to 3B parameters, pretrained on Tahoe-100M and fine-tuned for cancer tasks.

Open library of 177 Agent Skills and 100+ scientific database connectors that give coding agents validated procedures for biology, chemistry, medicine and drug discovery.

Phylogeny-aware genomic language model over whole-genome alignments and species trees for genome-wide variant effect and constraint prediction.

All-atom diffusion model from the Baker lab for designing protein binders to DNA, proteins and small molecules, plus enzymes; ~10x faster than RFdiffusion2.

Deep generative model in scvi-tools that integrates flow cytometry, mass cytometry and CITE-seq data across panels, with protein imputation, differential expression and annotation.

Baichuan's open 32B medical reasoning model trained with a patient simulator and clinical-rubric RL; reported as the top open model on HealthBench at release.

Pangenome-guided structural variant caller for long reads that can also add new samples to an existing pangenome graph.

NVIDIA latent flow model for joint atomistic protein sequence-structure generation.

DeepMind model predicting regulatory effects of DNA variants at base resolution.

Alibaba DAMO's multimodal medical LLMs (7B/32B) for medical image understanding, reasoning, VQA and report generation.

AI-ready DDA mass-spectrometry corpus (1.54B MS/MS spectra, 548M PSMs, 35 species) in a Parquet tensor format (MSDT), plus XuanjiNovo, a non-autoregressive transformer for de novo peptide sequencing trained on 100M PSMs.

General-purpose biomedical AI agent with 150+ tools and databases.

Arc Institute virtual-cell model predicting transcriptomic responses to perturbations across cell contexts.

Google's open Gemma 3 variants for medical text and image comprehension.

FutureHouse multi-agent system automating hypothesis generation and data analysis for drug discovery.

Meta FAIR's open dataset of DFT calculations on diverse molecules, with evaluations and baseline models.

CZI cross-species generative single-cell foundation model trained on 112M cells from 12 species.

Visual–omics foundation model aligning H&E histology with spatial transcriptomics for tissue alignment, cell-type decomposition and expression prediction.

Atom-level enzyme active-site scaffolding with diffusion, no sequence-position constraints.

Promptable segmentation for 3D medical images and videos, built on SAM 2.

Deep-learning tumor-only somatic small-variant caller for long reads, from the Clair3 team.

NVIDIA flow-matching model for large-scale protein backbone generation.

100M-cell single-cell drug perturbation atlas.

Genome language model across all domains of life with 1M-token context.

scGPT continually pretrained on spatial transcriptomics for spatial-aware embeddings, deconvolution and imputation.

SE(3) flow-matching model that generates RNA 3D structures and conformer ensembles conditioned on base pairing.

Bunne lab's spatial proteomics foundation model for multiplexed tissue imaging, from molecules to cells to tissue, for diagnostics and case retrieval.

7B-parameter metagenomic foundation model pretrained on 1.5T+ base pairs of human-wastewater DNA/RNA for pathogen detection and biosurveillance.

Open medical reasoning LLMs trained with verifiable medical problems and RL for complex clinical reasoning.

Trainable PyTorch reproduction of AlphaFold 3 by ByteDance.

Generative model emulating protein equilibrium structural ensembles.

Multimodal whole-slide pathology foundation model pretrained on 335,645 WSIs with vision–language alignment.

RNA language-model-based method for 3D RNA structure prediction.

Foundation model for joint segmentation, detection and recognition across nine biomedical imaging modalities.

Open biomolecular interaction models (Boltz-1, Boltz-2) for complex structure and binding-affinity prediction.

Bo Wang lab's mature-RNA foundation model trained with contrastive learning on splice isoforms and orthologs, for RNA half-life and other property prediction.

One-shot de novo binder design pipeline with AF2 hallucination.

Transformer-based polisher that reduces base errors in PacBio HiFi genome assemblies.

High-accuracy RAG agent over scientific papers.

Multimodal foundation model for molecular structure prediction.

Graph-based foundation model for spatial transcriptomics: zero-shot spatial domains and cross-slide/cross-technology integration.

Gymnasium for training language agents on scientific tasks (molecular cloning, literature QA).

Troyanskaya lab's sequence-to-expression transfer-learning model: predicts gene expression and variant effects across 802 developing-brain contexts (26 regions, 30 cell types, 7 stages).

Google's deep-learning somatic small-variant caller for tumor/normal data across Illumina, PacBio and Oxford Nanopore.

Tatta Bio's mixed-modality (protein + DNA) genomic language model trained on the OMG open metagenomic corpus.

Template-based protein design: edits an existing protein by insertions, deletions and substitutions while keeping its fold and function.

Python framework that runs bio foundation models such as Geneformer, scGPT, UCE and Evo 2 through one interface for embedding and fine-tuning, with an MCP integration for OpenAI Codex.

Multimodal generative protein language model over sequence, structure and function (ESM3; also ESM C embeddings).

Stanford's 3D vision-language model for abdominal CT, pretrained on scans, EHR codes and radiology reports for classification, retrieval and report generation.

100M-parameter foundation model trained on 50M human single-cell transcriptomes.

Scalable cell segmentation for subcellular-resolution spatial transcriptomics that combines staining images with transcript signals across platforms.

LLM agent that designs genetic perturbation experiments (e.g. CRISPR screens).

Whole-slide digital pathology foundation model.

Deep-learning error correction of nanopore simplex reads that enables telomere-to-telomere phased assemblies.

Pathology foundation model for cancer detection, tumor-origin and molecular-profile prediction and prognosis from whole slides.

Structure prediction of proteins, nucleic acids, ligands and their complexes; hosted web server plus open code, weights on request for non-commercial use.

LLM agent that plans and automates gene-editing experiments: system selection, guide design, protocols and analysis.

AI-designed Cas9-like gene editor from Profluent's language models, released openly.

Foundation model pretrained on dissociated and spatial single-cell data.

Vision-language foundation model for histopathology.

General-purpose self-supervised foundation model for computational pathology.

Structure-based de novo antibody and nanobody design with fine-tuned RFdiffusion, ProteinMPNN and RF2.

Latent diffusion model that predicts coarse-grained conformational ensembles of intrinsically disordered regions from sequence.

Bidirectional, reverse-complement-equivariant DNA language model.

Interpretable deep learning model that predicts cellular potency and absolute developmental potential from scRNA-seq.

HEST-1k dataset of 1,276 paired spatial transcriptomics and H&E whole-slide samples, plus a library and benchmark for histology foundation models.

General-purpose RNA language model that generalizes to unseen RNA families on secondary-structure prediction.

7B genomic foundation model (StripedHyena) for prokaryotic DNA, RNA and protein design at single-nucleotide resolution.

RNA foundation model trained on Ribonanza chemical-mapping data for structure and reactivity.

Open 7B medical LLM based on Mistral, further pretrained on PubMed Central.

Paired-chain antibody language model trained to reduce germline bias.

Toolkit for using trained genomic deep learning models: batched predictions, DeepLIFT/SHAP attributions, variant effects, ablations, seqlet calling, motif annotation and DNA design.

Contrastive multi-view representation learning for binning metagenomic contigs into genomes.

Protein sequence design that explicitly models small molecules, nucleotides and metals, and also outputs side-chain conformations.

GPT-4-driven agent that plans and executes chemistry experiments on lab hardware.

Benchmarks for protein fitness and mutation effect prediction.

Self-supervised transformer foundation model for tandem mass spectra of small molecules.

Cross-species zero-shot universal cell embedding foundation model.

Open medical LLMs (7B/70B) adapted from Llama 2 on curated medical corpora.

Comprehensive R toolbox for spatial data analysis and visualization.

Ultrafast species-level metagenome profiler using abundance-corrected k-mer sketching (ANI-based containment).

Foundation model for cell segmentation across imaging modalities, pairing a cell-detection prompter with SAM.

Generates full-atom ligand-binding protein pockets, co-designing residue sequence and structure.

AstraZeneca's generative molecular design framework.

Genomic language model over multi-species alignments for genome-wide variant effect prediction.

Generalized biomolecular modeling for proteins, nucleic acids, small molecules.

Protein language model with a structure-aware vocabulary combining amino acids with Foldseek 3Di tokens.

Proteome-wide pathogenicity predictions for 71M missense variants (repo archived; precomputed scores available).

Turns expression profiles into ranked gene "cell sentences" so LLMs (up to 27B) can model and generate single-cell data.

Predicts RNA-seq coverage from DNA sequence.

Transformer and diffusion models for de novo peptide sequencing from tandem mass spectra.

Graph-based prediction of transcriptional outcomes of genetic perturbations.

Plausibility checks and benchmark for docking/structure poses.

Graph neural network built on ProteinMPNN that predicts stability changes (ddG) for protein point mutations.

Hardware-agnostic Python interface for liquid-handling robots and lab automation.

Long-range genomic foundation model at single-nucleotide resolution.

Efficient multi-species genome foundation model.

Framework for LLM-based conversational biomedical research.

LLM chemistry agent augmented with 18 expert tools for synthesis and molecule tasks.

Benchmarking platform for ML in drug discovery.

Biomedical vision-language assistant trained on PubMed Central figure-caption data.

Transformer pretrained on ~30M single-cell transcriptomes for network biology predictions.

Deep learning structure prediction for antibodies, nanobodies and TCRs (ABodyBuilder2 etc.).

Geometric deep learning for 3D RNA inverse design (sequence design from backbone).

Generalist vision-language foundation model for biomedical images and text, covering VQA, captioning, report generation and classification.

Multi-omics single-cell optimal transport for space and time mapping.

Segment Anything fine-tuned for light and electron microscopy, with interactive napari annotation tools.

Missense variant pathogenicity model trained on primate population variation and 3D protein structure.

Generative pretrained transformer foundation model for single-cell multi-omics.

Deep-learning prediction of prime-editing efficiency across PE systems and cell types (Hyongbum Kim lab).

Segment Anything adapted to medical images: promptable segmentation across CT, MRI, ultrasound, endoscopy and more.

Genomic language model trained on metagenomic contigs that learns gene context to predict protein co-regulation and function.

Unified data framework for spatial omics technologies.

Fast antibody structure prediction using AntiBERTy embeddings and graph networks.

Microsoft's biomedical vision-language model pretrained on 15M PubMed Central figure–caption pairs.

DIA proteomics search engine without feature detection, with transfer learning from AlphaPeptDeep.

Marker-gene-based taxonomic profiling of metagenomes, including uncharacterized species.

SE(3) diffusion model for protein backbone generation over residue frames.

Living benchmarks for single-cell analysis tasks.

Bias-corrected base-resolution deep learning models of ATAC/DNase-seq chromatin accessibility and TF motifs.

Attention-based model predicting prime editing efficiency to design pegRNAs.

DNA foundation models pretrained on 3,200+ human and 850 species genomes.

Scalable identification and comparison of spatial cell niches across samples and technologies (scverse ecosystem).

Self-supervised deep learning metagenomic binning for short and long reads.

Curated single-cell data portal and Census API with 100M+ cells.

Diffusion model for de novo protein backbone design and binder design.

Oxford Nanopore's production basecaller: transformer/LSTM models turning raw nanopore signal into reads, with modified-base calling and duplex.

Diffusion generative model for molecular docking.

Equivariant diffusion model that designs small molecules directly inside a protein pocket.

Transformer for de novo peptide sequencing directly from MS/MS spectra.

Neural-network and marker-based identification of viruses and plasmids in sequencing data.

Graph self-supervised contrastive learning for spatial transcriptomics.

Protein language models and single-sequence structure prediction (Meta repo archived; models still widely used via Hugging Face).

Graph deep learning model that learns cell embeddings from communication programs to identify niches across spatial samples.

Machine-learning estimation of completeness and contamination of metagenome-assembled genomes.

Diffusion model that generates protein backbones by denoising internal backbone angles.

Deep learning framework predicting peptide MS2 spectra, retention time and CCS for proteomics.

Protein language models (up to 6.4B) for sequence generation and fitness prediction.

Broad GPP model for SpCas9 sgRNA on-target activity, used in CRISPick.

Deep-learning inverse folding: designs sequences for given backbones.

E(3)-equivariant graph neural network that samples drug-like molecules atom by atom inside 3D protein pockets.

Neighborhood-augmented clustering that unifies cell typing and tissue-domain segmentation in spatial omics.

Uses general protein language models to recommend affinity-improving mutations, applied to human antibodies.

Fast structural-variant caller for long reads with population-scale and mosaic SV detection.

RNA foundation model pretrained on 23M non-coding RNAs for structure and function tasks.

Cloud platform for bioinformatics workflows and data.

Geometric diffusion model that generates 3D molecular conformations from molecular graphs (ICLR 2022).

Fast and accurate protein structure search.

Spatial single-cell analysis toolkit in the scverse ecosystem.

Antibody language model for heavy/light chains, used for residue restoration and embeddings.

Transformer that corrects PacBio HiFi consensus reads, increasing yield and accuracy.

BERT-style antibody language model trained on 558M OAS sequences.

Models for detecting modified bases (e.g. 5mC, 6mA) from nanopore signal.

Trainable open-source reproduction of AlphaFold2 in PyTorch.

PyTorch platform for drug discovery machine learning: property prediction, generation and retrosynthesis.

Sequence-based deep learning model for single-cell ATAC-seq representation and TF activity.

Graph attention autoencoder for spatial domain identification.

Graph-linked variational autoencoders that integrate unpaired single-cell multi-omics data and infer regulatory interactions.

Antibody humanization (Sapiens) and humanness evaluation (OASis) platform.

Predicts 21,907 chromatin profiles from sequence and maps variants to regulatory sequence classes.

Fast, accessible protein folding with MMseqs2 + AlphaFold in Colab.

DeepMind's open-source protein structure prediction model (monomer and multimer).

Baker lab's three-track neural network for protein structure and protein-protein complex prediction.

Multimodal omics framework for single-cell multi-omics in Python.

Deep-learning germline small-variant caller for long reads (ONT, PacBio) and short reads.

Automated cell type annotation with logistic regression models trained on curated atlases.

Predicts gene expression from sequence with long-range interactions.

Optimal-transport alignment and integration of spatial transcriptomics slices.

Fast, widely used open-source molecular docking engine.

ML-ready datasets and benchmarks for therapeutics.

Siamese neural network predicting structural similarity between MS/MS spectra for metabolomics.

Unsupervised evolutionary VAE for predicting pathogenicity of missense variants.

In silico gene perturbation via GRN inference from single-cell multi-omics.

Functional profiling of metagenomes and metatranscriptomes (gene families and pathways).

Baidu's bio-computing platform built on PaddlePaddle, including HelixFold structure prediction and molecular representation models.

Cell fate mapping using Markov chains on single-cell data.

Graph convolutional network that combines gene expression, spatial location and histology to find spatial domains and spatially variable genes.

Bayesian mapping of fine-grained cell types in spatial transcriptomics.

Haplotype-aware variant calling pipeline for nanopore reads combining PEPPER, Margin and DeepVariant.

Probabilistic deep generative models for single-cell omics (scVI, totalVI, scANVI...).

Bayesian clustering and resolution enhancement of spatial transcriptomics.

Maps single-cell profiles onto spatial data with deep learning.

Fast image-like U-Net for RNA secondary structure prediction.

RNA secondary structure model trained via multitask learning on Eterna player data.

Python library for importing, cleaning and comparing mass spectra for metabolomics.

Transformer protein language models (ProtT5, ProtBERT and others) pretrained on UniRef and BFD, used for embeddings and fine-tuning.

Robust cell type decomposition for spatial transcriptomics.

Scalable single-cell chromatin accessibility (scATAC-seq) analysis in R.

Pretrained weighted graph neural network for cell-type annotation of scRNA-seq data.

All-atom protein structure dataset extending ProteinNet with sidechain angles and coordinates, with PyTorch loaders for ML.

Oxford Nanopore's research basecaller for training and testing new basecalling models.

PyTorch molecular dynamics engine that mixes classical force fields with neural network potentials.

Generalist deep learning model for cellular segmentation.

Deep learning retention-time predictor that generalizes to unseen peptide modifications.

Fast, interactive multi-dimensional image viewer for Python.

Deep learning RNA secondary structure prediction including non-canonical pairs and pseudoknots.

RNA velocity estimation with dynamical modeling.

NVIDIA's deep learning toolkit that denoises ATAC-seq coverage tracks and calls peaks from low-coverage or low-quality data.

Base-resolution neural network for TF binding profiles (ChIP-nexus/ChIP-seq) and motif syntax discovery.

Extensible platform for microbiome (amplicon and shotgun) data science.

Benchmark of five protein tasks with pretraining corpus and pretrained language model weights for evaluating protein embeddings.

Web tool for designing CRISPR-Cas9, Cas12 and TALEN targets with efficiency predictions.

Doublet detection for single-cell data using gradient-boosted trees trained on artificial doublets.

Metabolomics software for molecular formula and structure annotation from MS/MS with CSI:FingerID and CANOPUS.

Geometric deep learning on protein molecular surfaces to predict interaction sites, ligand pockets and binding partners.

Deep generative model that removes ambient RNA and other technical background from droplet scRNA-seq count matrices.

Message passing neural networks for molecular property prediction.

Predicts peptide fragmentation spectra (and with DeepLC/IM2Deep) for spectral libraries and rescoring.

Deep learning prediction of splicing effects from pre-mRNA sequence (repo archived).

Variational autoencoder for metagenomic binning.

Fast, sensitive batch integration of single-cell data.

Analysis of genome-editing outcomes from amplicon sequencing, including base and prime editing.

Siamese neural network for structure-preserving dimensionality reduction of very large single-cell datasets.

PyTorch library for training and applying sequence-level deep learning models (DeepSEA-style).

Fast k-mer based taxonomic classification of metagenomic reads.

Star-convex object detection for nuclei segmentation.

Van Valen lab deep learning library for cell segmentation (Mesmer) and tracking in 2D/3D biological images.

PyTorch library for training and running ANI neural-network interatomic potentials, maintained by the Roitberg group.

Python library for learning dynamical models from time series, from Markov state models to VAMPnets and deep MSMs.

Neural-network-powered data-independent acquisition (DIA) proteomics processing software.

Assigns GTDB taxonomy to bacterial and archaeal genomes and MAGs.

Neural-network consensus polishing and variant calling for nanopore assemblies.

Scalable Python toolkit for single-cell gene expression analysis.

Sequence-to-function CNNs predicting chromatin accessibility, histone marks and expression tracks from DNA.

Random-forest classifier on gene pairs that assigns cell types to scRNA-seq data across species and platforms.

Google deep learning variant caller for Illumina, PacBio HiFi and ONT data.

Open-source software for digital pathology and whole slide image analysis.

Web tool for CRISPR guide selection with on-target efficiency and off-target scoring for 500+ genomes.

Deep learning toolkit for drug discovery, materials and biology.

R toolkit for single-cell genomics, integration and multimodal analysis.

High-performance molecular dynamics toolkit with GPU support.

Standard RNA secondary structure prediction and design package (RNAfold, RNAinverse).

Image analysis software for high-throughput cell phenotyping.

Open-source platform for LC-MS and imaging-MS data processing in metabolomics.

Public single-cell and spatial (Visium, Xenium) example datasets.

Cloud lab for protein expression and binding characterization, binder design competitions.

200M+ predicted protein structures from DeepMind and EMBL-EBI.

R&D cloud with ELN, molecular biology and AI tooling.

Critical Assessment of protein Structure Prediction community experiment.

AI search engine over peer-reviewed research.

FutureHouse's hosted AI scientist agents (Crow, Falcon, Owl, Phoenix) for literature search, precedent search and chemistry.

AI research assistant for literature search and data extraction.

Remote-controlled cloud laboratory running 200+ instrument types.

Cell programming platform and automated lab services.

Hugging Face Hub listing of biology-tagged models, from protein and DNA language models to single-cell models.

Data portal of the Human Cell Atlas consortium.

NVIDIA platform and NIMs for training and deploying biology foundation models.

Open-source liquid-handling robots with a Python Protocol API and AI protocol generation.

Archive of experimentally determined 3D biomolecular structures.

Open-source cheminformatics and machine learning toolkit.

Cloud computational chemistry platform with ML-accelerated simulations.

Marketplace for outsourced R&D and CRO services.

Robotic cloud lab automation for life sciences.

No-code web platform running 100+ protein and molecular ML models.

Synthetic DNA, gene fragments and antibody discovery services.

Comprehensive resource of protein sequence and function.