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AI for chemical reaction prediction and synthesis planning

Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)

ECMWF's unified framework and command-line tool to run AI-based weather forecasting models (GraphCast, Aurora, Pangu, NeuralGCM, FourCastNet) with operational ECMWF data infrastructure, enabling standardized inference and benchmarking across state-of-the-art meteorological AI systems (ECMWF, 576+ stars)

Shanghai AI Lab's deep learning-based global weather forecasting model pushing skillful forecasts beyond 10 days lead, with open-source inference code and pretrained ONNX model weights (arXiv 2023)

Computation Pipeline library for python widely used in science and bioinformatics.

Open-source framework for building physics-ML models at scale (renamed from Modulus, 2025)

The Reagent Ontology (ReO) adheres to OBO Foundry principles (obofoundry.org) to model the domain of biomedical research reagents, considered broadly to include materials applied “chemically” in scientific techniques to facilitate generation of data and research materials. ReO is a modular ontology that re-uses existing ontologies to facilitate cross-domain interoperability. It consists of reagents and their properties, linking diverse biological and experimental entities to which they are related. ReO supports community use cases by providing a flexible, extensible, and deeply integrated framework that can be adapted and extended with more specific modeling to meet application needs.

E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)

AI-powered pipeline converting papers into interactive websites, posters, and multimedia presentations with "Let's Make Your Paper Alive!" philosophy

Transformer encoder-decoder for de novo peptide sequencing from tandem mass spectrometry, translating MS/MS spectra directly to peptide sequences without reference databases, enabling identification of novel peptides for immunopeptidomics, antibody repertoires, and metaproteomes (Noble Lab UW, Nature Communications 2024)

A library for building, manipulating, analyzing and automatic design of molecules, including a genetic algorithm.

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)

A Package For Training SNAP Interatomic Potentials for use in the LAMMPS molecular dynamics package.

Interactive and hardware-agnostic SDK for laboratory automation, enabling programmatic control of liquid handlers, plate readers, and other lab instruments across multiple vendors; foundational infrastructure for self-driving laboratories and AI-driven experimental execution (447+ stars)

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data.

Unified framework for state-of-the-art pre-trained bio foundation models across genomics and transcriptomics, providing standardized interfaces and pipelines for DNA, RNA, and single-cell models including Evo 2, Geneformer, scGPT, and UCE with streamlined inference, benchmarking, and fine-tuning workflows (213+ stars, 2024-2025)

ChemFormula provides a class for working with chemical formulas. It allows parsing chemical formulas, calculating formula weights, and generating formatted output strings (e.g. in HTML, LaTeX, or Unicode).

Equivariant graph attention Transformer (ICLR2023)

First agentic LLM for autonomous data science with end-to-end pipeline from data to analyst-grade reports

A Python script that converts positional information from a SAM dataset into interval format with 0-based start and 1-based end. CIGAR string of SAM format is used to compute the end coordinate.

Cross-platform library for differentiable programming of quantum computers with automatic differentiation, enabling hybrid quantum-classical machine learning for quantum chemistry, quantum physics, and NISQ algorithm research (Xanadu, 3k+ stars)

Universal pretrained neural network potential with charge and magnetic moment awareness, trained on 1.5M+ Materials Project inorganic structures for charge-informed molecular dynamics and phase diagram prediction (Berkeley, Nature Machine Intelligence 2023 Cover)

Deep learning library for Chemistry based on Tensorflow

Foundation model for universal cell segmentation achieving state-of-the-art performance across bacteria, tissue, yeast, cell culture, and diverse imaging modalities (brightfield, fluorescence, phase), with pip-installable inference and Napari plugin (vanvalenlab/Caltech, bioRxiv 2024)

Diffusion model for scalable protein structure design with multi-motif scaffolding capabilities, achieving state-of-the-art designability, diversity, and novelty through SE(3)-equivariant attention and massive data augmentation (AlQuraishi Lab, 2024)

Experiments with expanded ensembles to explore chemical space.

Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)

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)

Co-create PowerPoint presentations with Generative AI from documents or topics

A Python package for protein dynamics analysis

OpenChem is a deep learning toolkit for Computational Chemistry with PyTorch backend.

Pythonic Access to the Ensembl database.

The Context and Measurement Ontology (COMO) contains ontological terms to describe the context for various types of experimental data and measurements. It is useful in its current state for several different environmental microbiology projects. This ontology is used in multiple CORAL (Contextual Ontology-based Repository Analysis Library) deployments.

The Common Core Ontologies (CCO) comprise twelve ontologies that are designed to represent and integrate taxonomies of generic classes and relations across all domains of interest. CCO is a mid-level extension of Basic Formal Ontology (BFO), an upper-level ontology framework widely used to structure and integrate ontologies in the biomedical domain (Arp, et al., 2015). BFO aims to represent the most generic categories of entity and the most generic types of relations that hold between them, by defining a small number of classes and relations. CCO then extends from BFO in the sense that every class in CCO is asserted to be a subclass of some class in BFO, and that CCO adopts the generic relations defined in BFO (e.g., has_part) (Smith and Grenon, 2004). Accordingly, CCO classes and relations are heavily constrained by the BFO framework, from which it inherits much of its basic semantic relationships.

The Chromosome Ontology is an automatically derived ontology of chromosomes and chromosome parts.

The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance.

A data model for managing information about chemical entities, ranging from atoms through molecules to complex mixtures.

An extension of Schema.org to annotate metadata on software projects

An EMMO-based domain ontology for atomistic and electronic modelling.

Algorithm Metadata Vocabulary is a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). There are uncountable algorithms present in every area (e.g., Computer Science, Mathematics), which makes it hard for specialists, academicians, application engineers, and so forth to discover, distinguish, select, and reuse them. [from repository]

This ontology integrates cell type markers for cells in the Cell Ontology from various sources along with details of marker context (anatomical context, assay), confidence (where available) and provenance. [from repository]