/tools

Pysisyphus is a Python suite designed for exploring potential energy surfaces in both ground and excited states. It provides methods for searching stationary points and calculating minimum energy paths, making it a valuable tool for molecular simulations and quantum chemistry workflows.

This repository provides a collection of interactive Jupyter notebooks focused on quantum mechanics and computational materials science. It includes tutorials on molecular dynamics and other related concepts, making it a useful educational tool for understanding molecular behavior and simulations.

MSA Pairformer is a tool that extracts evolutionary signals from aligned homologous sequences to predict residue-residue interactions in proteins. It provides functionalities for embedding protein sequences and predicting contacts, making it useful for applications in protein design and analysis.

SMACT is a Python package designed for materials design and informatics, focusing on rapid screening of hypothetical materials and predicting their properties. It utilizes data about chemical elements to facilitate the generation and optimization of compositions, making it a valuable resource in computational chemistry.

Uni-GBSA is an automatic workflow designed to perform MM/GB(PB)SA calculations for evaluating ligand binding free energies in virtual screening. It includes functionalities for topology preparation, structure optimization, and batch processing of multiple ligands against a protein target.

LOOS is a lightweight object-oriented library designed for the structural analysis of molecular dynamics simulations. It includes various tools for analyzing molecular structures, clustering, and estimating statistical errors, making it useful for researchers in computational chemistry and molecular biology.

ProteinNPT is a semi-supervised model designed to predict and generate protein properties. It utilizes non-parametric transformers to learn representations of protein sequences and their associated properties, enabling both property prediction and iterative redesign of proteins.

MGSSL is an official implementation of a method for motif-based graph self-supervised learning aimed at predicting molecular properties. It includes pretraining and finetuning on the MoleculeNet dataset, making it a useful tool for molecular property prediction tasks.

FlowDock is a geometric flow matching model designed for generative protein-ligand docking and affinity prediction. It provides tools for predicting molecular interactions and includes datasets for training and evaluation, making it a valuable resource in computational chemistry and molecular biology.

MolGym is a tool that utilizes reinforcement learning to design molecules in three-dimensional space, guided by principles of quantum mechanics. It allows users to train agents to build molecular structures by placing atoms on a canvas, facilitating the generation and optimization of new molecular designs.

PSICHIC is a physicochemical graph neural network designed to learn protein-ligand interaction fingerprints from sequence data. It enables quick virtual screening and deep analysis of molecular interactions, making it a valuable tool for drug discovery.

g-xTB is a development version of a general-purpose semiempirical quantum mechanical method that approximates molecular properties. It supports geometry optimization and numerical gradient calculations, making it useful for various molecular simulations and analyses.

PXDesignBench is a comprehensive evaluation suite for protein design that integrates various state-of-the-art models and standardized pipelines. It supports tasks such as monomer and binder design, allowing for thorough assessment of protein sequences and structures.

The Deep Docking protocol is a deep learning-based tool that accelerates docking-based virtual screening, allowing researchers to screen extensive chemical libraries significantly faster than traditional methods. It integrates various stages of molecular preparation, docking, and model training to identify potential drug candidates effectively.

Awesome Molecular Dynamics is a curated collection of libraries, tools, and software focused on molecular dynamics. It includes various molecular dynamics engines, trajectory analysis tools, and visualization software, making it a comprehensive resource for researchers in computational chemistry and molecular biology.

dyMEAN is a tool for end-to-end full-atom antibody design, enabling the generation and optimization of antibody structures based on specific epitope definitions. It includes functionalities for complex structure prediction and affinity optimization, making it a valuable resource in drug discovery and protein design.

UPET is a tool for advanced atomistic simulations that utilizes machine-learned interatomic potentials to predict energies, forces, and other properties of materials and molecules. It supports various simulation engines and is designed for efficiency in high-performance computing environments.

kGCN is a graph-based deep learning framework that focuses on the classification and prediction of molecular properties using graph convolutional networks. It supports the generation of molecular data and provides tools for dataset preparation and model training in cheminformatics.

The xtb-python repository offers a Python API for the extended tight binding (xtb) program, facilitating access to quantum chemistry calculations. It allows users to perform molecular simulations and analyses without requiring a separate installation of the xtb software.

This repository provides a PyMOL extension that allows users to color protein structures derived from the AlphaFold Protein Structure Database according to their confidence levels (pLDDT). It enhances the visualization of protein structures, aiding in the analysis of structural biology data.

trRosetta2 provides deep learning models and scripts for predicting protein structures based on inter-residue geometries. It is designed to assist in the molecular design and optimization of protein structures, making it a valuable tool in computational biology.

DrugGPT is a tool that employs a GPT-based strategy to design potential ligands for specific proteins. It utilizes deep learning to explore chemical space and optimize ligand design, enhancing the drug development process.

PPIFlow is a framework for the de novo generation of high-affinity biological binders, including antibodies and nanobodies. It integrates a design workflow that supports various tasks such as binder design, antibody design, and motif scaffolding, utilizing deep learning techniques for protein structure generation.

The 'galaxytools' repository contains a collection of Galaxy Tool wrappers that facilitate the integration of cheminformatics and RNA bioinformatics tools into the Galaxy platform, enabling researchers to utilize these tools for molecular analysis and data processing.