/tools

AiZynthFinder is a Python tool that utilizes Monte Carlo tree search and neural networks for retrosynthetic planning, allowing users to break down complex molecules into purchasable precursors. It supports customizable search algorithms and is aimed at facilitating the design and generation of molecules.

RoseTTAFold All-Atom is a neural network designed for predicting the structures of various biomolecular assemblies, including proteins, nucleic acids, and small molecules. It provides functionalities for predicting protein-nucleic acid complexes and covalently modified proteins, making it a valuable tool in molecular biology and computational chemistry.

Dynamic PDB is a large-scale dataset that integrates dynamic behaviors and physical properties in protein structures, enhancing existing protein databases. It includes data from all-atom molecular dynamics simulations, capturing conformational changes and various physical attributes of proteins.

ChemicalX is a deep learning library designed for predicting drug-drug interactions and polypharmacy effects. It provides integrated benchmark datasets and state-of-the-art models for drug pair scoring, making it a valuable tool in the field of computational chemistry and drug discovery.

BioPandas provides tools for handling molecular structures, particularly from PDB and MOL2 files, using pandas DataFrames. It facilitates the analysis and manipulation of protein structures, making it useful for tasks in drug discovery and computational biology.

The xtb program package is designed for semiempirical quantum chemistry calculations, enabling users to perform molecular simulations and predict various molecular properties. It supports atomistic simulations and is applicable to a wide range of molecular types.

BioEmu is a tool that emulates protein equilibrium ensembles by sampling structures based on amino acid sequences using generative deep learning techniques. It includes features for steering samples towards physically plausible conformations and can be used for side-chain reconstruction and molecular dynamics equilibration.

OpenChem is a deep learning toolkit that facilitates computational chemistry and drug design research. It provides utilities for data preprocessing, model training, and supports various molecular data types, enabling tasks like classification, regression, and generative modeling.

AIRS is an open-source collection of software tools and datasets focused on artificial intelligence applications in quantum, atomistic, and continuum systems. It includes resources for predicting molecular properties, designing molecules, and conducting simulations, making it highly relevant to the fields of computational chemistry and molecular biology.

Thermo is an open-source software library designed for calculating thermodynamic and transport properties of chemicals and their mixtures. It facilitates the retrieval of chemical constants and the calculation of properties such as viscosity, density, and solubility, making it useful for molecular simulations and chemical engineering applications.

Poly is a Go package designed for engineering organisms, providing tools for tasks such as codon optimization and primer design. It aims to be a comprehensive resource for computational synthetic biology, making it useful for both academic and industrial applications.

GPUMD is a highly efficient molecular dynamics package designed for GPU acceleration. It supports the training and application of neuroevolution potentials, enabling advanced atomistic simulations for various materials.

Ketcher is an open-source web-based chemical structure editor designed for chemists and laboratory scientists. It provides features for drawing, editing, and visualizing molecular structures in various formats, supporting both small molecules and macromolecules.

MDTraj is an open-source Python library that allows users to read, write, and analyze molecular dynamics trajectories. It supports a wide variety of file formats and provides fast analysis functions, making it a valuable tool for researchers in molecular dynamics and computational chemistry.

Foundry is a central repository for biomolecular foundation models that provides tools for training and using models for protein design, including generative models and structure prediction. It relies on AtomWorks for manipulating biomolecular structures and supports various protein design tasks.

TorchMD is an end-to-end molecular dynamics engine that leverages PyTorch to facilitate molecular simulations. It provides a user-friendly API for researchers to conduct force-field development and integrate neural network potentials into molecular dynamics workflows.

DNA Features Viewer is a Python library designed to visualize DNA sequence features from GenBank or GFF files. It allows users to create clear plots of DNA sequences, making it useful for synthetic biology applications and DNA design.

REINVENT4 is an AI molecular design tool that focuses on generating and optimizing small molecules through techniques like reinforcement learning. It supports various design tasks such as scaffold hopping and R-group replacement, making it a valuable resource for drug discovery.

GFlowNet is a framework for generating diverse molecular candidates using flow network-based generative models. It is part of a project aimed at enhancing molecule discovery through advanced machine learning techniques.

PyRosetta.notebooks offers Jupyter Notebooks that serve as a learning resource for the PyRosetta platform, which is used for biomolecular structure prediction and design. The repository includes tutorials on protein folding, docking, and design, making it a valuable tool for researchers in computational biology and chemistry.

The BioNeMo Framework is a suite of tools and libraries optimized for training AI models in drug discovery, enabling efficient handling of biological data. It supports various molecular modeling tasks, including property prediction and molecular design, leveraging GPU resources for enhanced performance.

This repository contains demonstrations of quantum machine learning and quantum chemistry applications using the PennyLane library. It provides tutorials and implementations that showcase various techniques in quantum computing relevant to molecular research.

PLIP (Protein-Ligand Interaction Profiler) is a tool designed to analyze and visualize non-covalent interactions between proteins and ligands in PDB files. It facilitates the understanding of molecular interactions, which is crucial for applications in drug discovery and molecular biology.

Awesome Flow Matching is a collection of works focused on flow matching and stochastic interpolants, which are relevant for generative modeling in various domains, including molecular design. It includes applications for generating and optimizing molecular structures, particularly in the context of DNA sequence design.