/tools

MolOrbDraw is a visualization tool that allows users to interactively view molecular orbitals from Gaussian cube files. It provides advanced molecular structure analysis capabilities, including bond detection and topology analysis.

The neutromeratio tool calculates tautomeric ratios in solution using neural network potentials, enabling accurate predictions of molecular properties. It incorporates conformational sampling and quantum mechanical energy calculations to enhance the reliability of tautomer ratio determinations.

This repository provides a tutorial on how to analyze molecular dynamics simulations of membrane proteins using VMD and MDAnalysis. It includes examples of simple analysis tasks and encourages users to write their own analysis code.

RoyalMD is a lightweight molecular dynamics pipeline that automates the setup and execution of protein simulations. It supports various molecular systems, including proteins and nucleic acids, and is optimized for use on portable hardware.

FvHallucinator is a tool that designs antibody sequences to achieve desired folding structures using a deep learning model. It employs a sequence-to-structure prediction approach to optimize the design of specific regions within antibodies, particularly the complementarity-determining regions (CDRs).

NAMD-FEP is a tool designed for calculating the binding free energy difference between two small molecules interacting with the same protein target using free energy perturbation (FEP) methods. It provides a Jupyter Notebook tutorial for users to perform FEP simulations efficiently, leveraging cloud GPU resources.

p2smi is a Python toolkit designed for the generation and analysis of drug-like peptide SMILES strings. It allows users to create peptide sequences, convert them to SMILES representations, and evaluate various molecular properties, making it a valuable resource for computational peptide chemistry.

This repository provides a framework for using supervised machine learning to identify collective variables that can accelerate molecular simulations. It includes methods for generating these variables based on decision functions from classification algorithms, facilitating enhanced sampling techniques in molecular dynamics.

This repository contains Python scripts for conducting GPU-accelerated molecular dynamics simulations with the AMOEBA force field, specifically tailored for simulating ion channels and other biomolecules. It enhances OpenMM's capabilities with features for system preparation, trajectory conversion, and umbrella sampling simulations.

FraGAT is a fragment-oriented multi-scale graph attention model aimed at predicting molecular properties. It includes a dataset used for experiments and provides Python files to build and utilize the FraGAT model for molecular property predictions.

DeepDrugDomain is a Python toolkit designed for the preprocessing and prediction of drug-target interactions and binding affinities using deep learning techniques. It offers extensive preprocessing capabilities and built-in benchmarks, making it a valuable resource for researchers in computational drug discovery.

MDeePred is a tool designed for predicting the binding affinity between bioactive small molecules and target proteins using a novel protein featurization approach. It employs deep learning techniques to enhance the accuracy of predictions, making it useful for drug discovery and repositioning efforts.

AGF-phonon-transport is a Matlab implementation of the Atomistic Green's function method for calculating phonon transmission in materials. It is designed to simulate heat conduction in solids using interatomic potentials, making it relevant for molecular dynamics studies.

GPUQT is a CUDA-implemented software that efficiently calculates charge and spin transport properties of large molecular systems using linear-scaling quantum transport methods. It supports both CPU and GPU computations and is integrated into a broader molecular dynamics framework.

The knime-rdkit repository contains nodes that integrate RDKit functionalities into the KNIME Analytics Platform, facilitating various cheminformatics tasks. It supports molecular property predictions and data manipulation, which are essential for drug discovery and molecular design.

This repository provides a PyTorch implementation for generative protein design, allowing users to create and optimize protein structures. It includes scripts for dataset management and model training, making it a useful tool for researchers in molecular biology.

ML-ensemble-docking is a tool designed to enhance structure-based virtual screening by utilizing ensemble docking methods combined with machine learning techniques. It evaluates the performance of various protein targets and improves ligand ranking through advanced predictive models.

FRAME is a tool for fragment-based molecular expansion that utilizes geometric deep learning techniques to aid in structure-based ligand design. It allows for the addition of molecular fragments to a seed ligand, optimizing the design process for drug discovery.

DeepBioisostere is a tool that utilizes deep learning to perform bioisosteric replacements, optimizing various molecular properties such as molecular weight and logP. It allows users to generate and optimize molecules based on specified criteria, making it relevant for molecular design and property prediction.

GraphRelax is a drop-in replacement for Rosetta Relax that utilizes graph neural networks for residue repacking and design. It combines LigandMPNN for sequence design with OpenMM for energy minimization, enabling efficient protein structure optimization.

This tool implements the Confidence Bootstrapping procedure for enhancing protein-ligand docking predictions. It includes pretrained models and datasets for benchmarking, making it useful for researchers in molecular docking and drug discovery.

cuik-molmaker is a specialized package for molecular featurization that transforms chemical structures into formats compatible with deep learning models, particularly graph neural networks. It combines C++ and Python for efficient processing and is designed to facilitate the training and inference workflows in molecular machine learning.