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Fast, interactive, multi-dimensional image viewer for Python, foundational platform for scientific imaging AI with a rich plugin ecosystem integrating deep learning segmentation, object tracking, and microscopy analysis workflows (2.6K+ stars)

From https://anndata.readthedocs.io/en/latest/ "Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray."

Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)

MEG and EEG.

Manipulation and analysis of geometric objects.

Probabilistic framework for inferring cell fate decisions and trajectory dynamics from multi-view single-cell data using Markov chains and machine learning, integrating RNA velocity, pseudotime, and metabolic labeling to predict differentiation paths and terminal states (scverse/Theis Lab, 449+ stars, BSD 3-Clause)

Deep learning-based multi-animal pose tracking and behavior classification, enabling automated quantification of social interactions and collective behavior across species (Nature Methods 2022, 2.2K+ stars)

Parsers and algorithms for computational chemistry logfiles.

15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)

Cheminformatics toolkit

Scalable toolkit for analyzing single-cell gene expression data, including preprocessing, visualization, clustering, and trajectory inference.

A package for working with nuclear magnetic resonance (NMR) data including functions for reading common binary file formats and processing NMR data.

Parallel computing with task scheduling.

Web application and service for visualizing small- to medium-scale models of gene regulatory networks. It automatically lays out either an unweighted or weighted network graph based on an Excel input spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows. It is best-suited for visualizing networks of fewer than 35 nodes and 70 edges and has general applicability.

Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)

A tool and library for creating quantum chemistry input files.

Deep probabilistic framework for single-cell and spatial omics analysis, integrating scVI, scANVI, totalVI and other VAE-based models for batch correction, cell annotation, multi-omics integration, and RNA velocity (scverse/NumFOCUS, Nature Methods 2018/2024)

An interactive structure/property explorer for materials and molecules.

Python astronomy tools

Calculate mass, elemental composition, and mass distribution spectrum of a molecule given by its chemical formula, relative element weights, or sequence.

Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference

A Workflow Management System geared towards scientific workflows.

Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)

Makes alchemical free energy calculations easier by leveraging the full power and flexibility of the PyData stack.

CalibraCurve is a computational tool designed to generate calibration curves for targeted mass spectrometry-based quantitative data. It is applicable to various omics disciplines, including proteomics, lipidomics, and metabolomics. The package also offers functionalities for data and calibration curve visualization and concentration prediction from new datasets based on the established curves.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.

Structural variant discovery by integrated paired-end and split-read analysis.

This package provides an R wrapper for the popular Bowtie2 sequencing read aligner, optimized to run on NVIDIA graphics cards. It includes wrapper functions that enable both genome indexing and alignment to the generated indexes, ensuring high performance and ease of use within the R environment.

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data. (based on Tensorflow)

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Deep learning-based variant caller

A library containing basis sets for use in quantum chemistry calculations. In addition, this library has functionality for manipulation of basis set data.

Deep learning-based object detection and segmentation for star-convex shapes, widely adopted for cell and nucleus segmentation in fluorescence and electron microscopy via a compact neural network architecture with non-maximum suppression and shape-based post-processing (Nature Methods 2020, 1.2K+ stars)

flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment.

Foundation model jointly trained on single-cell and spatial transcriptomics data, enabling unified representation learning across cellular and tissue spatial contexts for cell type prediction, spatial domain inference, and cross-modal integration (theislab, bioRxiv 2024, 164+ stars)

State-specific protein-ligand complex structure prediction with a multi-scale deep generative model, enabling conformational state-aware modeling of molecular interactions (329+ stars, 2024)

A module for solving and visualizing the Schrödinger equation.

zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.

Library with several compositional and structural material descriptors, along with a few pre-trained neural network models of material properties.

A Deep Learning Library for Compound and Protein Modeling DTI, Drug Property, PPI, DDI, Protein Function Prediction.

Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data

Molecular descriptor calculator based on [RDKit](http://www.rdkit.org/).

Psi4-based reference implementations and Jupyter notebook-based tutorials for foundational quantum chemistry methods.

This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation.

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

Jupyter Widgets to interact with molecular datasets.