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(Diffraction Integration for Advanced Light Sources) a software project for the analysis of crystallographic diffraction images for many new methods from shutterless, fine-sliced rotation scans to randomly-oriented snapshots of serial crystallography. It uses multiple CPU and GPU machines to increase speed and accuracy based on a comprehensive physical model.

a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities.

a set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections.

a program for analysis of SAXS data that enables creation of 1D scattering profiles from 2D detector images, standard data operations such as averaging and subtraction, analysis of radius of gyration (Rg) and molecular weight, and advanced processing using GNOM, DAMMIF, and AMBIMETER. It also allows easy processing of inline SEC-SAXS data.

a tool of the next-generation molecular visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. Compared to Chimera, ChimeraX has improved graphics (e.g. interactive ambient shadows); faster handling of large structures (millions of atoms); and a more modern, single-window user interface.

an ultra fast and sensitive sequence search and clustering suite

a tool of the next-generation molecular visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. Compared to Chimera, ChimeraX has improved graphics (e.g. interactive ambient shadows); faster handling of large structures (millions of atoms); and a more modern, single-window user interface.

a set of tools for cryo-EM and cryo-ET data processing including, among other tools: Warp, M, WarpTools, MTools, MCore, and Noise2Map.

is a Python-based framework for analyzing electron microscopy data within a unified, Matlab-like environment. The package provides a distinctive correlation-based docking methodology that uses cross-validation to define confidence intervals for solutions. It also includes tools for denoising, segmentation, and pattern recognition, and is designed to integrate numerous external data-processing packages through a single interface.

Vina-CUDA, QuickVina2-CUDA, and QuickVina-W-CUDA based on Vina-GPU 2.1, QuickVina2-GPU 2.1, and QuickVina-W-GPU 2.1 further enhance Vina-GPU 2.1 and its variants docking speed by optimizing its core algorithms through deep utilization of GPU hardware features, thereby reducing the cost of virtual screening and increasing the efficiency of drug design.