/tools

protPy is a Python package designed to calculate a variety of physicochemical, biochemical, and structural descriptors for proteins. It utilizes sequence-derived features of amino acids to aid in applications such as protein engineering and predicting protein structure and function.

Boltz-sample is a tool that extends the Boltz-2 framework to enhance conformational sampling of proteins by scaling the latent pair representation. It allows users to explore alternative protein conformations and evaluate predictions against reference structures, making it useful for protein structure prediction and molecular simulations.

DeepLBVS is a tool for ligand-based virtual screening that utilizes deep learning techniques to predict molecular properties. It generates ECFP fingerprints and performs cross-validation using RandomForest models to assess assay results, making it useful for drug discovery applications.

pyRIF is a Python tool that utilizes Rotamer Interaction Fields to assist in protein design by aligning input poses with target structures. It integrates with PyRosetta and RIFDock to generate and apply rotamer interaction fields for optimizing protein interactions.

This repository implements a method for training neural network potentials for coarse-grained proteins using unsupervised learning. It allows for the simulation of proteins and the prediction of native-like conformations through molecular dynamics, making it a valuable tool in computational chemistry.

PyGromosTools is a Python library designed to facilitate the use of GROMOS for molecular simulations. It offers wrappers for simulation execution, file handling for various GROMOS file types, and tools for analyzing molecular dynamics, making it a comprehensive tool for researchers in computational chemistry.

The 'mol-ellipsize' tool calculates the size of molecules by fitting an ellipsoid around their van der Waals cloud based on multiple conformers. It utilizes RDKit for generating conformer ensembles and provides functionality for molecular size analysis, making it useful in computational chemistry applications.

SAAINT is a structural antibody parser and database that facilitates the extraction and annotation of antibody structures and their interactions with antigens from the Protein Data Bank. It provides tools for building and analyzing a comprehensive antibody database, making it useful for antibody modeling and design.

Deepbiologic provides deep learning tools aimed at improving the design of biologic therapeutics. It seeks to enhance open-source resources in the area of biologics, which are less developed compared to small molecule drug design.

The Flexible Topology project develops a machine learning-based tool for dynamically designing potential drug molecules. It utilizes PyTorch to create models that predict molecular structures and poses, and incorporates molecular dynamics simulations to optimize these designs toward drug-like candidates.

The 'hilbert' repository is a tool that integrates with the Psi4 quantum chemistry software to facilitate molecular simulations and calculations. It provides methods for generating and optimizing molecular properties, leveraging the capabilities of Psi4 for computational chemistry tasks.

DashMD provides real-time monitoring and visualization of Amber molecular dynamics simulations. It allows users to track various simulation parameters and visualize molecular structures and trajectories interactively.

VenusX is a benchmark tool designed for fine-grained functional annotation of proteins, focusing on tasks such as residue-level classification and fragment-level classification. It includes a comprehensive dataset with over 878,000 samples, facilitating the evaluation of protein models and their functional understanding.

The _map_align_ tool aligns two contact maps to maximize overlapping contacts while minimizing gaps. It allows users to input contact maps and template libraries, facilitating the alignment of protein structures based on contact information.

ChemBounce is a Python tool that facilitates scaffold hopping by generating new molecular structures based on input SMILES. It uses pre-computed fingerprints for efficient similarity searches and allows users to customize scaffold databases for enhanced performance.

P4ward is an automated pipeline for modeling Protacs ternary complexes, allowing researchers to generate and analyze complex structures. It utilizes open-source tools for structural biology and provides interactive visualizations and summary tables of the modeling results.

Mongo-rdkit is a project that integrates RDKit, a cheminformatics toolkit, with MongoDB to facilitate the creation and manipulation of a chemically-intelligent database. It provides methods for performing high-performance searches, including similarity and substructure searches, making it useful for molecular property analysis.

WatCon is a Python tool that analyzes conserved water networks in both static structures and dynamic trajectories of proteins. It facilitates the visualization of these networks and their interactions across protein families, contributing to the understanding of molecular interactions.

AlphaCutter is a Python tool that efficiently removes non-globular regions from predicted protein structures. It utilizes parameters to define and filter out specific structural features, aiding in the refinement of protein models.

Transport Analysis is a Python package designed to compute and analyze transport properties of molecular systems. It is part of the MDAnalysis project and facilitates the study of molecular dynamics and transport phenomena.

TrimCI is a high-performance framework designed for quantum many-body and quantum chemistry calculations, enabling the discovery of accurate ground states from random Slater determinants. It achieves state-of-the-art accuracy and efficiency in molecular systems, making it a valuable tool for computational chemistry.

CP2K containers facilitate the use of CP2K software for molecular dynamics and quantum chemistry simulations. The containers are designed for various host systems and support running on HPC environments, making it easier to perform complex molecular simulations.

The `chemprop-contrib` repository contains community-contributed extensions to the Chemprop machine learning package, which is designed for predicting molecular properties. It allows users to implement various models and functionalities that enhance the capabilities of Chemprop in the context of molecular machine learning.