/tools

The RDKit Tutorials repository offers a collection of tutorials designed to help users learn how to effectively utilize the RDKit toolkit for various molecular tasks. It covers practical applications in cheminformatics, including molecular property predictions and manipulations.

Bio is an open-source AI assistant designed for biomedical research, allowing users to access academic literature, clinical trials, and drug information through natural language queries. It also supports advanced analytics and pharmacokinetic modeling, making it a valuable resource for drug discovery and molecular property analysis.

The DPLM repository provides implementations of diffusion protein language models that excel in generating and predicting protein sequences and structures. It includes features for unconditional and conditional protein generation, as well as representation learning for various protein-related tasks.

ProstT5 is a bilingual language model designed for translating between protein sequences and their corresponding 3D structures. It utilizes advanced machine learning techniques to derive embeddings and facilitate the understanding of protein structures from sequences.

xyz2mol is a Python tool that converts Cartesian coordinates from xyz files into RDKit molecular objects, allowing for the generation of molecular graphs. It supports the handling of resonance forms and can output various molecular formats, making it useful for computational chemistry applications.

gmx_MMPBSA is a tool designed to perform end-state free energy calculations with GROMACS files, leveraging the MMPBSA method. It is based on AMBER's MMPBSA.py and is compatible with various versions of GROMACS and AmberTools.

CREST (Conformer-Rotamer Ensemble Sampling Tool) automates the exploration of low-energy molecular chemical space using efficient force-field and semiempirical quantum mechanical methods. It provides capabilities for creating and analyzing molecular structure ensembles, making it a valuable tool for computational chemistry.

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.

This repository contains input files and data for LAMMPS, a molecular dynamics simulation code. It includes various examples of molecular simulations, such as interactions of different molecules and materials, which can be used for research in computational chemistry and materials science.

VASPy is a Python library that facilitates the manipulation and visualization of VASP files, which are used in computational chemistry. It allows users to process various types of VASP data, including electronic structure and molecular dynamics simulations.

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.

OpenFermion-Cirq provides quantum circuits designed for simulating quantum chemistry and materials. Although it is deprecated, its functionality is relevant for molecular simulations and understanding electronic structures in molecules.

The Ersilia Model Hub is a platform that hosts pre-trained AI/ML models aimed at drug discovery, particularly for infectious and neglected diseases. It includes models for predicting antibiotic activity, ADMET properties, and generative chemistry, facilitating research in molecular biology and computational chemistry.

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.

Equiformer is a PyTorch implementation of an SE3/E3 equivariant attention network that achieves state-of-the-art results in protein folding. It utilizes advanced techniques in deep learning to model molecular interactions and predict protein structures.

Mol2vec is an unsupervised machine learning tool that generates vector representations of molecular substructures, facilitating the analysis and prediction of molecular properties. It allows users to prepare molecular data, train models, and featurize new samples using learned embeddings.

IBM's FM4M is a multi-modal foundation model designed to support research in materials science and chemistry. It includes various pre-trained models for predicting molecular properties and generating molecular representations, making it a versatile tool for computational chemistry applications.

Chemformer is a repository that implements a pre-trained transformer model for generating and predicting molecular properties, including reaction and retrosynthetic predictions. It utilizes SMILES strings for molecular representation and is aimed at enhancing molecular design and optimization tasks.

SchNet is a deep learning architecture that provides insights into quantum-mechanical observables of atomistic systems. It is used for predicting properties such as total energy and forces in molecular systems, making it a valuable tool in computational chemistry.

The 'stk' library is designed for the construction and manipulation of complex molecules, facilitating automatic molecular design and the creation of molecular databases. It serves as a framework for researchers in computational chemistry and materials science to explore molecular structures and properties.

DynamicBind is a computational tool that predicts ligand-specific structures of protein-ligand complexes using a deep equivariant generative model. It facilitates the docking process by generating and ranking multiple poses, providing insights into binding affinities and conformational changes.

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.

This repository provides supporting materials for a book on molecular dynamics simulation, including sample codes and examples that demonstrate various aspects of molecular dynamics, such as classical physics, empirical potentials, and machine-learned potentials.

InterPLM is a toolkit designed for extracting, analyzing, and visualizing interpretable features from protein language models using sparse autoencoders. It allows users to work with protein embeddings and provides pretrained models for feature analysis and visualization.