/tools

Chai-1 is a multi-modal foundation model for predicting the structures of various biomolecules, including proteins, small molecules, DNA, and RNA. It utilizes advanced techniques to achieve state-of-the-art performance in molecular structure prediction across multiple benchmarks.

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.

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.

OpenFold3-preview is an open-source biomolecular structure prediction model that aims to replicate the capabilities of AlphaFold3. It supports the prediction of structures for proteins, RNA, and DNA, and includes benchmarking against state-of-the-art models.

ProLIF (Protein-Ligand Interaction Fingerprints) is a tool that generates interaction fingerprints for complexes involving ligands, proteins, DNA, or RNA. It utilizes data from molecular dynamics trajectories and docking simulations, making it valuable for drug discovery and cheminformatics.

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.

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.

ATOMICA is a geometric AI model that learns universal representations of intermolecular interactions at an atomic scale. It is pretrained on a large dataset of molecular interaction interfaces and can be used for various downstream tasks, including binding site prediction and embedding biomolecular complexes.

ModelAngelo is an automatic atomic model building program designed for cryo-electron microscopy (cryo-EM) maps. It allows users to build models of proteins and nucleic acids from cryo-EM data, facilitating the identification and modeling of molecular structures.

PENSA is a Python library designed for the exploratory analysis and comparison of biomolecular conformational ensembles, particularly from molecular dynamics simulations. It offers functionalities such as structural ensemble comparison, clustering, and trajectory processing, making it a valuable tool for researchers in molecular biology and biophysics.

Molchanica is a comprehensive tool for editing, visualizing, and simulating molecules and proteins. It includes features for molecular dynamics, docking, and predicting pharmacokinetic properties, making it suitable for drug discovery and molecular design.

The 'galaxytools' repository contains a collection of Galaxy Tool wrappers that facilitate the integration of cheminformatics and RNA bioinformatics tools into the Galaxy platform, enabling researchers to utilize these tools for molecular analysis and data processing.

The 'arnie' repository provides a Python API for estimating and comparing RNA energetics across various secondary structure algorithms. It facilitates structure prediction and analysis of RNA sequences, making it a useful tool in molecular biology.

FoldBench is a benchmarking tool for all-atom biomolecular structure prediction, enabling the evaluation of models across various biomolecular interactions and monomeric structures. It includes a comprehensive dataset for proteins, nucleic acids, and ligands, facilitating assessments of prediction accuracy in molecular simulations.

RNAFlow is a tool for designing RNA sequences and structures using a flow matching model that incorporates inverse folding techniques. It allows users to generate RNA sequences conditioned on protein structures, facilitating the design of RNA molecules for various applications.

GEMORNA is a deep generative model that designs mRNA sequences with enhanced translational capacity and stability. It supports the generation of coding sequences and untranslated regions, making it a valuable tool for advancing mRNA therapeutics and vaccines.

This repository provides an open-source solution for conducting computational studies in drug discovery using both quantum and classical computing resources. It includes sample code for various drug discovery problems, such as molecular docking and protein folding, facilitating research in these areas.

pepp'r is a package designed for the evaluation of predicted molecular poses, allowing users to compute various metrics to assess the quality of structure predictions. It supports a wide range of metrics applicable to small molecules, proteins, and nucleic acid complexes.

PAMNet is a universal framework designed for accurate and efficient geometric deep learning of molecular systems. It excels in predicting molecular properties, such as binding affinities and RNA 3D structures, and utilizes graph neural networks to enhance performance in these tasks.

This repository serves as a companion to the book 'Machine Learning for Drug Discovery', providing code and data for various machine learning techniques applied to drug discovery. It covers topics such as ligand-based screening, generative models for de novo design, and structure-based drug design, making it a valuable resource for researchers in the field.

py2Dmol is a Python library designed for visualizing protein, DNA, and RNA structures in 2D, suitable for use in Google Colab and Jupyter notebooks. It allows users to load and display molecular structures interactively, making it a useful tool for molecular representation.

PrismNet is a deep learning framework designed to predict dynamic cellular protein-RNA interactions by utilizing in vivo RNA structure. It includes scripts for training models, evaluating performance, and preparing datasets for research in molecular biology.

oxDNA is a simulation code designed for modeling DNA and RNA using a coarse-grained approach. It provides an extensible framework for simulating various molecular interactions and dynamics, supporting both single CPU and GPU computations.

RNA3DB is a dataset of non-redundant RNA structures from the PDB, designed for training and benchmarking deep learning models focused on RNA structure prediction. It includes various RNA chains labeled with non-coding RNA families and provides tools for customizing and building the dataset.