/tools

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.

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 openmmforcefields repository offers CHARMM and AMBER force fields for use with OpenMM, enabling the parameterization of biomolecular systems and small molecules. It supports various force fields and provides tools for generating parameters for small molecules, making it a valuable resource for molecular simulations.

REINVENT is a tool for molecular de novo design that employs recurrent neural networks and reinforcement learning techniques. It allows users to explore chemical space and generate novel molecular structures based on learned representations.

Meeko is an interface for AutoDock that prepares input files for molecular docking and processes the output. It parameterizes both small organic molecules and biological macromolecules, facilitating drug discovery and molecular modeling.

Mol-Instructions is a dataset that contains a large collection of instructions for biomolecular tasks, including molecule-oriented and protein-oriented tasks. It aims to facilitate the development of large language models for generating and understanding molecular and protein-related information.

DeepDTA is a tool designed for predicting the binding affinity between drugs and their target proteins using deep learning techniques. It utilizes convolutional neural networks to model protein sequences and molecular representations, making it relevant for drug discovery and molecular property prediction.

The All-atom Diffusion Transformers repository provides an implementation of a generative model that can create new molecular and material structures using a unified latent diffusion framework. It supports the generation of both small molecules and periodic materials, making it a valuable tool for molecular design and materials science.

The 'torsional-diffusion' repository provides an implementation of a state-of-the-art method for generating molecular conformers using a diffusion framework. It outperforms traditional software in generating diverse molecular structures, making it a valuable tool for molecular design and optimization.

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.

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.

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.

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.

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.

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.

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.

Bio-Diffusion is a geometry-complete diffusion generative model designed for generating and optimizing 3D molecular structures. It allows for both unconditional and property-conditional generation of small molecules, making it a valuable tool in molecular design and optimization.

nvMolKit is a GPU-accelerated library that facilitates key computational chemistry tasks, including molecular similarity assessments and conformer generation. It is optimized for performance on NVIDIA GPUs, making it suitable for various molecular modeling applications.

PaDELPy is a Python wrapper for the PaDEL-Descriptor software, enabling users to calculate molecular descriptors and fingerprints from SMILES strings, MDL MolFiles, and SDF files. It facilitates property prediction and cheminformatics tasks by providing a straightforward interface for descriptor calculation.

This repository provides a hands-on tutorial on using graph neural networks (GNNs) for various applications in life sciences, including predicting properties of small and macro-molecules, and drug discovery. It includes practical sessions on training GNN models for molecular property prediction and binding affinity prediction for protein-ligand pairs.

MolecularGraph.jl is a graph-based toolkit designed for cheminformatics and molecular modeling. It offers functionalities for analyzing molecular properties, performing substructure queries, and visualizing molecular graphs, making it a valuable resource for computational chemistry applications.

PyAutoFEP is an automated workflow for Free Energy Perturbation (FEP) calculations using GROMACS, aimed at estimating the Relative Free Energies of Binding (RFEB) of small molecules to macromolecular targets. It integrates enhanced sampling methods and provides automation for various steps in the FEP process, making it accessible for both experts and non-experts.