/tools

The MDAnalysis Toolkits Registry is a repository that hosts and documents various MDAKits, which are tools designed for molecular simulation analysis. It aims to provide a structured approach to developing and validating tools relevant to molecular dynamics and computational chemistry.

Playmol is a software tool for building molecular models using simple scripts. It facilitates the creation of complex molecular systems and generates files compatible with molecular dynamics simulation software like LAMMPS and OpenMM.

Open3SPN2 is an implementation of the 3SPN2 and 3SPN2.C coarse-grained molecular models of DNA designed for use with OpenMM. It facilitates molecular simulations and dynamics, particularly focusing on DNA and its interactions with proteins.

ColabMDA is a user-friendly tool designed for molecular dynamics simulations, integrating Modeller for homology modeling and OpenMM for protein simulations. It facilitates the preparation, execution, and analysis of molecular dynamics workflows in a Google Colab environment.

This repository provides input files for GROMACS, a software package for performing molecular dynamics simulations. It includes examples for simulating various molecular systems, such as PEG in vacuum and sodium chloride solutions.

GROMACS-Windows-Builder provides binary builds of GROMACS for Windows, enabling users to perform molecular dynamics simulations. It includes support for various force fields and is optimized for performance on compatible hardware.

Dragonfly_gen is a tool for de novo drug design that utilizes deep interactome learning to generate novel molecules based on predefined properties. It allows users to preprocess data, sample from binding sites, and rank generated molecules based on pharmacophore similarity.

LMetalSite is a tool that predicts metal ion-binding sites from protein sequences using a pretrained language model and multi-task learning. It provides datasets and trained models for users interested in reproducing the results, making it a valuable resource in the field of molecular biology.

This repository provides code and data for predicting protein-ligand binding affinity using deep learning techniques. It includes various encodings for proteins and ligands, and offers datasets for training and testing models in the context of drug discovery.

DMFF-DTA is a dual-modality neural network designed for accurate drug-target affinity prediction by integrating sequence and structural information. It utilizes a binding site-focused graph construction method and provides a processed dataset for training.

The ChEMBL Multitask Neural Network model is a fast and efficient tool for predicting molecular targets based on a variety of bioactivity data. It supports off-target predictions and is implemented in multiple programming languages, making it versatile for different computational environments.

SCISOR is a diffusion model that generates shrunken protein sequences by learning from evolutionary data. It aims to optimize protein design by suggesting deletions that preserve functional motifs, making it a valuable tool in molecular biology.

The ProteinLM-TDG2-Mutation repository provides code for optimizing a uracil-N-glycosylase variant using protein language models. It facilitates the generation of mutations and benchmarking of models in the context of programmable base editing.

GATSol is a tool that enhances the prediction of protein solubility by leveraging 3D structural data and large language models. Users can prepare protein data in specific formats and utilize the tool to obtain solubility predictions, making it relevant for computational biology and molecular property prediction.

The SMILES Featurizers repository offers a set of tools to extract molecular embeddings from SMILES strings using pre-trained language models like BERT and BART. This functionality is useful for applications in molecular property prediction and virtual screening.

PySSA is a Python client designed for visual protein sequence to structure analysis, enabling users to predict and analyze 3D protein structures. It integrates with PyMOL and ColabFold, making protein structure prediction accessible for research and educational purposes.

The Interactive Mutation Browser is a tool that utilizes protein language models to facilitate interactive exploration of protein mutations. It aims to enhance the understanding and application of protein structures through innovative interactions with large language models.

MSIBI is a Python package designed for managing and running coarse-grain potential optimizations through the MultiState Iterative Boltzmann Inversion method. It facilitates the optimization of both intra-molecular and inter-molecular potentials, making it a valuable tool for molecular dynamics simulations.

This repository offers an automated workflow for converting SMILES representations of homopolymers to BigSMILES format and vice versa. It includes a dataset of BigSMILES representations, facilitating research in molecular representation and potential applications in deep learning.

ProtHGT is a model designed for predicting protein functions by integrating diverse biological datasets into a knowledge graph. It utilizes a heterogeneous graph transformer architecture to learn complex relationships and make accurate predictions across various Gene Ontology categories.

RFDpoly is a diffusion-based machine learning model that facilitates the de novo design of various polymers, including DNA, RNA, and proteins. It provides tools for generating molecular structures and optimizing their designs, making it a valuable resource in molecular biology and computational chemistry.

STCRpy is a software suite designed for analyzing and processing T-cell receptor (TCR) structures. It provides tools for interaction profiling, geometry calculations, and generating datasets compatible with machine learning frameworks, making it useful for researchers in molecular biology and immunology.

Bridge-IF is an implementation of a model for learning inverse protein folding using Markov Bridges. It utilizes datasets related to protein structures to train and evaluate the model for generating protein sequences based on their structures.