/tools

EquiDock is a tool designed for fast rigid protein-protein docking using independent SE(3)-equivariant models. It includes preprocessing steps for datasets and allows for training and inference of docking models, making it relevant for molecular simulations and drug discovery.

This repository serves as a comprehensive resource for machine learning in drug discovery, offering curated datasets, benchmarks, and educational materials. It focuses on enhancing the understanding and application of cheminformatics in predicting molecular properties and interactions.

MoleculeSTM is a multi-modal model designed for text-based editing and retrieval of molecular structures. It provides tools for molecular property prediction and includes datasets for training and evaluation, making it a valuable resource in drug discovery and molecular design.

GAUCHE is a library that facilitates probabilistic modeling and optimization techniques tailored for molecular representations. It includes various kernels for molecules, chemical reactions, and proteins, enabling users to perform tasks such as property prediction and Bayesian optimization.

The Open Free Energy toolkit (`openfe`) is a Python package designed for executing alchemical free energy calculations, primarily aimed at enhancing molecular simulations in drug discovery. It provides robust tools for planning and executing these calculations, contributing to the field of computational chemistry.

mols2grid is an interactive molecule viewer designed for visualizing 2D structures of molecules. It utilizes RDKit for cheminformatics tasks and provides a user-friendly interface for exploring molecular data.

ACPYPE is a Python tool that interfaces with Antechamber to generate topologies for chemical compounds, facilitating their use in molecular dynamics simulations with software like GROMACS and CHARMM. It supports various molecular types and is designed to work with compatible force fields.

quacc is a flexible platform designed for computational materials science and quantum chemistry, enabling users to run various workflows for molecular simulations and property predictions efficiently. It integrates with existing resources and provides a unified interface for managing computational tasks.

Awesome-Biomolecule-Language-Cross-Modeling is a curated list of resources that focuses on leveraging biomolecule data and natural language processing through multi-modal learning. It includes various models and datasets that facilitate tasks related to molecular properties and interactions.

RadonPy is an open-source Python library that automates the calculation of various physical properties for polymers through all-atom classical molecular dynamics simulations. It supports the generation of polymer structures, force field assignments, and the calculation of numerous polymer properties, making it a comprehensive tool for polymer informatics.

Sumo is a Python toolkit designed for plotting and analyzing data from ab initio solid-state calculations. It offers features for generating k-point paths and creating publication-ready plots of electronic and phonon band structures, density of states, and optical absorption diagrams.

The 'mosaic' repository provides a framework for functional, multi-objective protein design using continuous relaxation and machine learning models. It allows users to combine various predictors to optimize protein properties such as binding affinity and solubility, making it a valuable tool for computational biology and molecular design.

GEOM is a dataset containing 37 million molecular conformations annotated by energy and statistical weight for over 450,000 molecules. It is designed for use in property prediction and molecular generation, providing essential data for researchers in computational chemistry.

Atomsk is a command-line tool that allows users to manipulate and convert atomic data files, facilitating the construction of crystals and polycrystals, as well as the introduction of defects in atomic systems. It supports various file formats and is useful in the context of materials science and atomic simulations.

Ankh is an optimized protein language model that enhances general-purpose modeling for protein engineering. It offers pre-trained models and datasets for various protein-related tasks, including secondary structure prediction and solubility assessment.

Colvars is a software library that facilitates molecular simulation and analysis by implementing sampling algorithms based on collective variables. It integrates with popular molecular dynamics packages like NAMD, LAMMPS, and GROMACS, enabling advanced simulation techniques and free-energy calculations.

Doped is a Python software designed for the generation, pre-/post-processing, and analysis of defect supercell calculations in materials. It implements a defect simulation workflow that allows users to efficiently analyze defect formation energies, charge states, and thermodynamic properties in semiconductors.

The MISATO repository offers a machine learning dataset of protein-ligand complexes designed for structure-based drug discovery. It includes molecular dynamics simulations and quantum mechanics data, facilitating the training of AI models for predicting binding affinities and other molecular properties.

MoleculeKit is a molecule manipulation library that facilitates the handling and visualization of molecular structures. It is particularly useful for tasks in drug discovery and molecular dynamics simulations.

LiGAN is a deep generative model designed for structure-based drug discovery, specifically generating 3D molecular structures that are predicted to bind to target proteins. It utilizes atomic density grids and is built on frameworks like PyTorch and MolGrid, making it a powerful tool for molecular design and optimization.

alchemlyb is a library designed to facilitate alchemical free energy calculations by providing parsers for molecular dynamics output files, subsamplers for timeseries data, and estimators for free energy calculations using established methods.

The 'py4chemoinformatics' repository provides resources and tools for chemoinformatics, including methods for predicting molecular properties, generating chemical structures, and utilizing machine learning techniques in molecular design and analysis. It serves as a comprehensive guide for researchers in the field.

MHCflurry is a software package designed for predicting the binding affinity of peptides to MHC class I molecules. It provides a fast and accurate implementation for various MHC alleles, making it a valuable tool for researchers in immunology and drug development.

The ChEMBL Structure Pipeline provides protocols for standardizing and salt stripping molecules, facilitating the preparation of chemical structures for further analysis. It includes functionalities for checking the quality of molecular structures and retrieving parent compounds, making it a valuable tool in molecular data processing.