/tools

cclib is a Python library designed to parse output files from various computational chemistry packages, enabling researchers to implement and utilize algorithms for analyzing molecular data in a platform-independent manner.

Psi4NumPy combines the Psi4 electronic structure package with NumPy to create an interactive environment for quantum chemistry education and development. It includes reference implementations of quantum chemical methods and tutorials for learning how to program these methods.

ProteinGym is a comprehensive repository of benchmarks for Deep Mutational Scanning (DMS) assays, allowing for the evaluation of various mutation effect predictors. It includes extensive datasets of clinical variants and experimental measurements, facilitating research in protein design and mutation effect prediction.

P2Rank is a command-line tool that predicts ligand-binding sites from protein structures using machine learning techniques. It provides high accuracy in identifying potential binding pockets without relying on external databases, making it useful for drug discovery and virtual screening applications.

Pocket2Mol is a tool designed for efficient molecular sampling based on the 3D structures of protein pockets. It employs equivariant graph neural networks to improve the quality and efficiency of molecular generation, making it useful for structure-based drug design.

The Open Forcefield Toolkit is a Python toolkit designed for the development and application of molecular mechanics force fields, specifically utilizing the SMIRNOFF format. It provides tools for creating and manipulating molecular systems, facilitating molecular simulations and dynamics.

LightDock is a docking framework that utilizes the Glowworm Swarm Optimization algorithm to facilitate the docking of proteins, peptides, and DNA. It allows users to define custom scoring functions and apply residue restraints, making it a versatile tool for studying molecular interactions.

The Rosetta Bio-macromolecule modeling package is a comprehensive suite for computational modeling and analysis of protein structures. It includes algorithms for de novo protein design, enzyme design, and ligand docking, facilitating significant advancements in computational biology.

Indigo is a universal cheminformatics toolkit that includes a variety of utilities for chemistry search, molecule visualization, and SMILES generation. It supports multiple programming languages and is designed for applications in drug discovery and molecular analysis.

QMCPACK is an open-source software package that utilizes many-body ab initio Quantum Monte Carlo methods to compute the electronic structure of various molecular and solid-state systems. It is optimized for high-performance computing environments and supports GPU acceleration.

This repository serves as a directory for various Fortran codes related to computational chemistry and molecular dynamics. It includes tools for simulating molecular interactions and properties, making it a valuable resource for researchers in the field.

GT4SD is an open-source library designed to accelerate hypothesis generation in scientific discovery, particularly in the fields of molecular design and property prediction. It includes generative models for creating molecules and tools for predicting molecular properties, making it a valuable resource for researchers in computational chemistry and molecular biology.

JARVIS-Tools is an open-source software package designed for data-driven atomistic materials design. It provides capabilities for setting up calculations, analysis, and informatics, as well as database and web-page development.

ReLeaSE is a tool that utilizes deep reinforcement learning to facilitate the de-novo design of drug molecules. It includes functionalities for optimizing molecular properties and generating new molecular structures based on learned patterns.

REINVENT is a tool designed for the de novo design of molecules using reinforcement learning techniques. It provides a framework for generating molecular structures and optimizing their properties, making it a valuable resource in the field of drug discovery.

CHGNet is a universal neural network potential that enables charge-informed atomistic modeling with high accuracy. It can predict molecular properties such as energy and forces, and is capable of running molecular dynamics simulations, making it a valuable tool in computational chemistry and materials science.

This repository provides a Tensorflow implementation of a Graph Convolutional Policy Network aimed at goal-directed molecular graph generation. It allows for the generation of molecules based on desired properties, utilizing reinforcement learning techniques.

BoFire is a Bayesian optimization framework designed for real experiments, particularly in the chemical and pharmaceutical industries. It supports optimization of molecular properties and experimental designs, utilizing advanced techniques like multi-objective optimization and active learning.

PyXtal is an open-source Python library that facilitates the generation of atomic and molecular structures while adhering to symmetry constraints. It includes features for geometry optimization and supports various structural formats for further analysis.

PoseBusters is a tool designed to perform plausibility checks on generated molecule poses, particularly in the context of docking studies. It allows users to validate the accuracy of predicted ligand poses against known protein-ligand complexes, contributing to the assessment of molecular docking methods.

RXNMapper is a tool that enables robust atom mapping on valid reaction SMILES using an unsupervised attention-guided approach. It utilizes a trained ALBERT model to extract atom mapping information from a large dataset of chemical reactions, facilitating the analysis and understanding of organic chemistry grammar.

The SMILES Transformer is a tool that extracts molecular fingerprints from SMILES representations of chemical molecules. It utilizes a transformer architecture to learn latent representations useful for various downstream tasks in drug discovery.

This repository is a survey of scientific large language models, particularly in biology and chemistry. It collects papers and resources related to the application of LLMs in these fields, but does not offer direct molecular tools or software.

DeepFRI is a tool designed for deep functional residue identification in proteins. It predicts protein functions based on sequences and contact maps using graph convolutional networks, providing insights into molecular functions and biological processes.