/tools

EasyDock is a Python module designed to automate the molecular docking process, from molecule preparation to result analysis. It supports various docking programs and offers features like distributed computing and detailed protein-ligand interaction analysis.

SIRIUS is a framework designed for metabolomics mass spectrometry, enabling the identification of molecular formulas for small molecules. It includes various modules for isotope pattern analysis, fragmentation tree computation, and compound class prediction.

The 'walk-jump' repository provides an open-source implementation of discrete Walk-Jump Sampling (dWJS) for protein design. It includes functionalities for training models and sampling, aimed at discovering and optimizing protein sequences.

AaronTools.py is a Python library designed to automate routine tasks in quantum chemistry computations. It offers features for generating molecular structures, calculating steric parameters, and setting up quantum computation processes, making it a valuable tool for computational chemists.

AF2BIND is a tool designed to predict ligand-binding sites in proteins using the AlphaFold2 model. It leverages the internal pairwise representation of AlphaFold2 to identify small-molecule-binding residues, enhancing the understanding of protein-ligand interactions.

Psi4Education offers a suite of computational chemistry lab activities designed for educational purposes. It includes both WebMO labs for quantum chemistry computations and Python labs for data collection and analysis within a Jupyter notebook environment.

Chemkit is an open source C++ library that facilitates molecular modeling and cheminformatics, providing tools for molecular visualization. It aims to support various applications in the field of chemistry through its comprehensive library functionalities.

LifeBrush is a VR toolkit that allows users to create and simulate biomolecular environments using a generative brush for painting molecular structures. It employs an agent-based modeling framework to visualize and interact with molecular dynamics in a 3D space.

This tool utilizes neural machine translation techniques to predict the outcomes of organic chemistry reactions based on known reactants. It employs an attention mechanism to enhance the prediction accuracy and provides a graphical user interface for user interaction.

This repository contains the implementation of MoT, a transformer-based model for predicting molecular properties from 3D molecular structures. It was developed as part of the Samsung AI Challenge for Scientific Discovery and utilizes large-scale datasets from PubChem for training and evaluation.

The SAMPL6 repository contains challenge inputs and results for predicting molecular properties, specifically focusing on pKa and logP values of small molecules. It serves as a benchmark for evaluating computational methods in predicting these properties, providing datasets and performance evaluations for participants.

FlexDock is a tool designed for flexible docking and relaxation of molecular complexes, aiming to improve the accuracy of docking predictions. It includes functionalities for preparing input data, running models, and training on relevant datasets, making it useful for researchers in computational chemistry and drug discovery.

MolSnapper is a software tool that utilizes a conditioned diffusion model to generate 3D drug-like molecules. It is built on the MolDiff framework and is aimed at aiding in structure-based drug design.

The OpenMM Cookbook offers a collection of tutorials and notebooks for users to learn how to utilize OpenMM for molecular dynamics simulations. It serves as a resource for both beginners and advanced users in the field of computational chemistry.

The nmrgnn repository contains a graph neural network model for predicting NMR chemical shifts from protein structures and organic molecules. It includes functionalities for evaluating structures and analyzing molecular trajectories, making it a valuable tool in computational chemistry.

Chemistry Drawer is a Python package that allows users to draw and visualize molecular structures using Plotly. It provides customizable aesthetics for atoms, bonds, and rings, making it a useful tool for representing chemical structures.

This tool provides modifications for VASP to control occupation matrices during DFT+U calculations, allowing users to set specific electronic occupations and achieve localization at specific sites. It is useful for studying electronic structures and optimizing molecular configurations.

KERMT is a pretrained graph neural network model that focuses on predicting molecular properties, particularly for small molecules. It enhances the GROVER model by automating hyperparameter tuning and supports distributed pretraining, making it a valuable resource for molecular property prediction tasks.

GLaDOS is a web interface for ChEMBL, a comprehensive database that provides information on drug discovery, including molecular structures and drug-target interactions. It facilitates access to cheminformatics data, supporting research in molecular biology and chemistry.

Metis is a Python-based GUI designed to collect expert feedback on small molecules, facilitating the training of generative models for drug discovery. It supports de novo design and integrates with existing generative chemistry frameworks to enhance molecular design processes.

Lemon is a framework that allows users to rapidly mine structural information from the Protein Data Bank. It enables the creation of standardized workflows for querying 3D features of macromolecules, enhancing the efficiency of structural biology research.

LDM-3DG implements a pipeline for latent 3D graph diffusion, enabling the generation and evaluation of molecular structures. It includes functionalities for training models on molecular data and generating embeddings for small molecules, making it a valuable tool for molecular design and docking applications.

CaLM is a codon adaptation language model designed to provide embeddings for DNA sequences, specifically aimed at enhancing protein engineering efforts. It allows users to embed sequences, which can be useful for predicting and optimizing protein properties.