/tools

DMPfold is a deep learning tool for de novo protein structure prediction that uses iteratively predicted structural constraints to model protein structures from sequences. It is designed to extend the coverage of protein modeling across genomes and provides a framework for generating multiple structural models for a given protein sequence.

ABFE_workflow is a SnakeMake-based workflow designed for performing Absolute Binding Free Energy calculations using GROMACS. It allows for high-throughput scaling and is aimed at facilitating drug discovery by predicting binding affinities between ligands and protein receptors.

The METL framework provides tools for pretraining and finetuning biophysics-informed protein language models, enabling users to train models on mutational data and generate predictions. It includes datasets for training and examples for running inference, making it a valuable resource for protein engineering and design.

PLM-interact is a tool that extends protein language models to predict protein-protein interactions. It utilizes a novel method to jointly encode protein pairs, enhancing the prediction of their interactions based on their sequences.

The EGNO repository implements an Equivariant Graph Neural Operator designed for modeling 3D dynamics, particularly in the context of molecular dynamics simulations. It includes data preprocessing for various datasets, including those relevant to protein molecular dynamics, making it a useful tool for researchers in computational chemistry.

Grappa is a machine learned molecular mechanics force field that utilizes graph neural networks to predict bonded parameters for molecular simulations. It integrates with GROMACS and OpenMM, allowing users to parametrize systems and train custom models using various molecular datasets.

The 'salad' repository provides a framework for generating protein structures using sparse all-atom denoising models. It allows for various protein design tasks, including unconditional generation and motif scaffolding, leveraging advanced machine learning techniques.

The PDN repository provides a PyTorch implementation of Pathfinder Discovery Networks, a method for learning message passing graphs. It is designed for tasks such as node classification, which can be applied to molecular data and analysis.

ConformationalBiasing is a computational tool designed for creating protein variants that favor specific conformational states. It utilizes inverse-folding models to score and predict the effects of mutations on protein function, making it a valuable resource for protein engineering and design.

Wepy is a modular framework for conducting weighted ensemble simulations in Python, designed to facilitate molecular dynamics and enhance sampling methods. It supports integration with OpenMM for molecular simulations and aims to provide a user-friendly interface for various simulation tasks.

Dock-MD-FEP is an open-source tool designed for automated binding free energy calculations using free energy perturbation methods. It provides a comprehensive workflow for docking and molecular dynamics simulations, specifically targeting interactions between proteins and small molecules.

The `pair_nequip_allegro` repository offers pair styles for LAMMPS that integrate deep learning models from the NequIP and Allegro frameworks, enabling advanced molecular dynamics simulations. It allows users to leverage machine learning for interatomic potential modeling in molecular simulations.

This repository provides scripts and resources for performing docking and free energy calculations related to the COVID Moonshot initiative. It includes tools for preparing receptors and ligands, as well as analyzing results from molecular simulations.

The py-rcsb-api is a Python toolkit designed to streamline access to the RCSB Protein Data Bank's API services. It allows users to perform complex queries to retrieve structural data about proteins and other macromolecules, facilitating research in molecular biology.

The `physical_validation` package is designed to test the physical validity of results obtained from molecular simulations. It provides tools for assessing whether simulation outcomes are consistent with physical principles, thereby enhancing the reliability of molecular modeling efforts.

The 'walk-jump' repository provides an implementation of discrete Walk-Jump Sampling (dWJS) for training and sampling in protein design. It includes functionalities for evaluating large molecule descriptors and assessing sample quality, making it a valuable tool in molecular design and optimization.

Cryo-IEF is a foundation model designed for cryo-electron microscopy (cryo-EM) image processing, enabling the classification and quality assessment of cryo-EM particle images. It supports the development of automated pipelines for analyzing biological macromolecules, making it a valuable tool in structural biology.

PyQInt is an educational Python implementation of the Hartree-Fock method, designed to provide transparency in electronic structure calculations. It supports various molecular integrals and geometry optimization, making it a useful tool for learning and prototyping in computational chemistry.

This repository contains input files and Python scripts for LAMMPS tutorials, focusing on molecular dynamics simulations. It provides resources for generating molecular structures and analyzing simulation data, making it a valuable tool for computational chemistry and molecular biology applications.

Ringtail is a Python package that facilitates the storage and analysis of virtual screening results from AutoDock-GPU and AutoDock Vina. It allows users to create SQLite databases from docking results, perform filtering, and export data, making it a valuable tool for molecular docking studies.

OMTRA is a multi-task generative model that facilitates structure-based drug design by generating novel molecules and performing protein-ligand docking. It supports various tasks including unconditional and conditioned generation of ligands, making it a valuable tool in computational chemistry and drug discovery.

NA-MPNN is a tool for RNA sequence design and predicting protein-DNA specificity. It provides training and inference code for generating sequences and evaluating specificity, making it relevant for molecular design applications.

GraphRetro is a tool designed for predicting retrosynthesis pathways using graph models. It employs a two-stage process to transform product molecules back to their reactants, making it useful for molecular design and synthesis planning.

The OpenFF Evaluator is a toolkit developed by the Open Forcefield Consortium for the automated estimation of physical property datasets derived from molecular simulations. It provides a scalable framework for evaluating various molecular properties, making it a valuable resource for computational chemistry and molecular biology applications.