/tools

DEERpredict is a software package designed for the prediction of double electron-electron resonance (DEER) and paramagnetic relaxation enhancement (PRE) data derived from molecular dynamics ensembles. It allows users to calculate intensity ratios and PRE rates for various conformations, making it a valuable tool in the field of molecular simulations.

The Willard-Chandler Instantaneous Interface Calculator is a Python-based tool designed to calculate instantaneous interfaces and concentration/orientation profiles from molecular simulation trajectories in slab geometry. It utilizes the marching cubes algorithm and is applicable for analyzing molecular dynamics data.

HackaMol is an object-oriented Perl library that facilitates computational work on molecular structures at multiple scales. It provides methods for organizing and manipulating atoms and molecules, making it useful for tasks in molecular modeling and simulations.

RobertoMD.jl is a Julia package designed for massively parallel hybrid particle-field molecular dynamics simulations. It implements a specific algorithm for simulating polymer systems and is aimed at providing a productive simulation environment for molecular dynamics research.

EGAT is a repository that utilizes Edge-Featured Graph Attention Networks to predict molecular properties and reactions. It includes functionalities for generating molecular graphs and training models for various prediction tasks related to molecular properties.

This repository provides a PyTorch implementation of a model aimed at understanding the limitations of deep learning in predicting molecular properties. It includes code for various machine learning models and datasets related to molecular property prediction.

AIMNet-X2D is a scalable Graph Neural Network framework that enables multi-task learning for predicting various molecular properties. It is designed to handle datasets of varying sizes efficiently and supports the creation of domain-specific molecular foundation models.

MGDTA is a tool designed for predicting drug-target binding affinity using multigranular representations. It includes datasets for training and evaluation, making it a valuable resource for researchers in the field of drug discovery.

ECIFGraph is a tool designed to predict protein-ligand binding affinities by utilizing a water network-augmented two-state model. It integrates deep learning techniques to enhance the accuracy of scoring functions in molecular docking and virtual screening applications.

HGRL-DTA is a PyTorch implementation designed for predicting drug-target binding affinity using hierarchical graph representation learning. It utilizes datasets like Davis and KIBA to train and evaluate its model, making it a valuable tool in the field of drug discovery.

The bcpaff repository explores the prediction of protein-ligand binding affinities using electron density-based geometric deep learning techniques. It provides tools for data processing and machine learning model training, specifically aimed at enhancing the understanding of molecular interactions.

KinaseDocker² is a computational tool that automates the docking and scoring of kinase inhibitors using a Deep Neural Network. It can be used as a PyMOL plugin or through the command line, leveraging GPU-accelerated docking methods.

pyqchem is a Python repository that contains routines for performing electronic structure calculations on atoms and small molecules. It allows users to execute various quantum chemistry methods, such as MP2, by modifying the script parameters.

marimo_chem_utils is a collection of utility functions designed for cheminformatics applications within marimo notebooks. It facilitates the analysis and visualization of molecular data, including generating molecular fingerprints and interactive plots.

The dftd3 package serves as an interface for the DFT-D3 method, allowing users to compute molecular energies and gradients. It is a drop-in replacement for the pyscf.dftd3 module, facilitating quantum chemical calculations for molecular systems.

ML4NMR is a machine learning-based tool designed to correct computed NMR chemical shifts from DFT calculations towards higher accuracy. It includes functionalities for data acquisition and model training, specifically targeting the prediction of NMR properties for small molecules.

MaskedProteinEnT provides code to sample protein sequences using a contextual Masked EnTransformer. It allows for the design and generation of protein sequences, making it a useful tool in the field of molecular biology and protein engineering.

This repository contains conda recipes for deploying software developed by the Glotzer Lab, specifically for high-performance computing resources. It facilitates the installation of HOOMD-blue, a toolkit used for molecular dynamics simulations.

The 'rev-sim' repository provides code and data for reversible molecular simulation aimed at training classical and machine learning force fields. It includes scripts for running simulations and benchmarks, making it useful for molecular dynamics and related computational chemistry tasks.

SecretoGen is a conditional autoregressive model that generates signal peptides based on the mature protein sequence and the host organism. It also includes functionality for evaluating the efficiency of these peptides, making it a specialized tool for protein design.

The repository includes Jupyter Notebooks that utilize the b2btools for predicting various biophysical properties of proteins, such as dynamics and folding propensities. It allows users to analyze both single protein sequences and multiple sequence alignments to explore their biophysical behavior.

BBB-score is a script that utilizes RDKit to reproduce the Blood-Brain Barrier score as reported in scientific literature. It serves as a tool for predicting molecular properties relevant to drug design and discovery.

MolCraftDiffusion is a unified generative-AI framework that facilitates the training and deployment of 3D molecular diffusion models for various molecular generation tasks. It supports property-targeted generation and provides tools for analyzing and optimizing generated molecules.