/tools

RFDesign is a tool for protein hallucination and inpainting using the RoseTTAFold framework. It allows for the generation of protein structures, which is a key aspect of molecular design and computational biology.

TensorMol is a Python-based tool that integrates TensorFlow with molecular simulations, allowing for the training of neural network force fields. It supports various simulation types, including molecular dynamics and Monte Carlo methods, and is capable of optimizing molecular geometries and predicting dynamic properties.

Jupyter Dock is a collection of Jupyter Notebooks that facilitate interactive molecular docking protocols, allowing users to visualize, convert file formats, and analyze docking results. It supports various docking methods and provides comprehensive protocols for different docking scenarios.

Uni-Dock is a GPU-accelerated molecular docking program designed to enhance the speed and accuracy of virtual screening processes. It supports various scoring functions and includes tools for handling input and output, as well as benchmarks for evaluating performance against public datasets.

This repository contains a series of Google Colab tutorials focused on structural bioinformatics, covering topics such as protein folding, molecular dynamics simulations, and molecular docking. It serves as an educational resource for learning about molecular modeling and simulation techniques.

ProteinFlow is an open-source Python library that streamlines the pre-processing of protein structure data for deep learning applications. It enables users to filter, cluster, and generate datasets from protein structure databases, facilitating various protein design tasks.

HTMD is a programmable platform for simulation-based molecular discovery, focusing on increasing reproducibility and solving data generation and analysis challenges in molecular simulations.

The mmpdb package facilitates the identification of matched molecular pairs to predict changes in molecular properties and generate new molecular structures. It supports fragmentation of molecules and indexing for analysis, making it a valuable tool in computational chemistry and drug discovery.

PLINDER is a dataset and evaluation resource focused on protein-ligand interactions, containing over 400k systems and numerous annotations for training and benchmarking docking algorithms. It aims to standardize the evaluation of protein-ligand interactions in the field of computational chemistry.

Kekule.js is an open-source JavaScript toolkit designed for cheminformatics, enabling users to read, write, display, and edit chemical objects. It provides functionalities for performing various cheminformatics tasks, including molecule comparison and structure searching.

GeoLDM is a tool for generating 3D molecular structures using geometric latent diffusion models. It supports conditional generation based on molecular properties and includes capabilities for evaluating the quality of generated molecules, making it useful for applications in drug discovery.

Equiformer is a PyTorch implementation of a graph attention transformer that processes 3D atomistic graphs, aimed at improving molecular simulations and property predictions. It supports training on established datasets like QM9 and MD17, making it a valuable tool for drug discovery and molecular dynamics research.

gpu4pyscf is a plugin that enables GPU acceleration for the PySCF package, facilitating efficient quantum chemistry calculations such as SCF and DFT. It includes features for geometry optimization and supports various molecular types, enhancing the performance of molecular simulations.

ProteinWorkshop is a benchmarking framework designed for protein representation learning. It includes a variety of pre-training and downstream task datasets, models, and utilities, making it a valuable resource for researchers in molecular biology and computational chemistry.

La-Proteina is a tool for the joint generation of protein amino acid sequences and their full atomistic structures. It utilizes a partially latent flow matching model to effectively design proteins, achieving state-of-the-art performance in various generation benchmarks.

MolScribe is an image-to-graph model that converts molecular images into their corresponding chemical structures. It employs deep learning techniques to facilitate molecular structure recognition, making it a valuable tool for researchers in computational chemistry and related fields.

OpenComplex is an open-source platform designed for developing protein and RNA complex models, leveraging features from AlphaFold 2 and OpenFold. It allows for high-precision modeling and inference of RNA and protein-RNA complexes, making it a valuable tool in computational biology.

ANARCI is a tool for antibody numbering and antigen receptor classification, utilizing alignment to germline sequences for accurate numbering. It supports various numbering schemes and outputs detailed alignment statistics, making it valuable for researchers working with antibodies.

ESPResSo is a versatile molecular dynamics software package that enables the simulation and analysis of many-particle systems using coarse-grained models. It is applicable in various fields including physics, chemistry, and molecular biology, allowing for the study of polymers, colloids, and biological systems.

ADMET-AI is a platform for predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of molecules using Chemprop models trained on relevant datasets. It provides functionalities through a command line tool, a Python API, and a web server for making predictions on new molecules.

molplotly is an add-on for Plotly that allows users to display 2D images of molecules in interactive figures. It utilizes RDKit for molecular visualization and is useful for data exploration in cheminformatics.

gptchem is a tool that leverages GPT-3 to solve chemistry problems, specifically focusing on predicting molecular properties through a classifier model. It is designed for experimentation and documentation in the field of computational chemistry.

TxGNN is a graph neural network model that predicts therapeutic opportunities for diseases using a comprehensive knowledge graph. It enables zero-shot inference for new diseases and can be trained on drug repurposing datasets, making it a valuable tool in drug discovery.

mzmine is an open-source software designed for processing mass spectrometry data, providing a comprehensive set of modules for analyzing various types of mass spectrometry data, including liquid and gas chromatography. It aims to facilitate the analysis of molecular properties through mass spectrometry techniques.