/tools

VOTCA is a software package that includes VOTCA-CSG for developing coarse-grained potentials from atomistic simulations and VOTCA-XTP for calculating electronically excited states and dynamics in molecular materials. It is designed for molecular simulations and quantum-classical embedding, making it a valuable tool in computational chemistry.

NRI-MD implements a neural relational inference model to learn long-range interactions in proteins from molecular dynamics simulations. It enables the analysis of allosteric communication pathways, providing insights into protein behavior and interactions based on simulation data.

GrASP is a tool that utilizes graph neural networks to predict druggable binding sites in proteins. It provides datasets and a framework for evaluating binding site predictions, making it useful for drug discovery applications.

The mdCATH repository contains scripts and notebooks for generating and analyzing the mdCATH dataset, which is focused on molecular dynamics trajectories. It provides tools for users to visualize and work with the dataset, making it useful for research in computational biophysics.

DMPfold2 is a fast and accurate method for predicting protein structures from sequence alignments. It utilizes deep learning techniques to generate models, enabling high-throughput exploration of uncharacterized proteins.

FASMIFRA is a tool for generating molecules quickly from a training set of molecular fragments using a method that matches the distribution of the training set. It also includes functionality for fragmenting molecules and has been evaluated in the GuacaMol benchmark for molecular generation methods.

Equiformer Diffusion is an implementation of denoising diffusion specifically aimed at protein design, utilizing the Equiformer architecture. It incorporates advanced techniques for generating and optimizing protein structures, making it a valuable tool in molecular design.

OpenFermion-ProjectQ is a plugin that allows for circuit simulation and compilation in quantum computing, specifically targeting fermionic systems. It interfaces with OpenFermion to facilitate the analysis and compilation of quantum algorithms related to electronic structure.

AutoDock CrankPep (ADCP) is a specialized docking engine for peptides that utilizes Monte-Carlo search methods to optimize peptide-receptor interactions. It allows for the docking of peptides provided as 3D structures or sequence strings, making it a valuable tool in molecular docking and protein design.

Chemoton is a Python framework that automates the exploration of complex chemical reaction networks using quantum chemical methods. It allows users to build workflows that investigate the reactivity of chemical systems, integrating with various quantum chemical software programs.

OpenQDC is an open-source hub that consolidates over 40 quantum mechanics datasets, making them readily available for machine learning applications in molecular property prediction. It supports the download of a vast array of quantum data, facilitating research in computational chemistry.

The cp2k-input-tools repository offers a set of pure-Python tools for creating, validating, and converting CP2K input files. It includes features for generating new input configurations and parsing existing ones, facilitating the use of CP2K for molecular simulations.

This tool models human 5' UTR sequences to predict their impact on translation and enables the design of new sequences for optimal protein expression. It utilizes deep learning and genetic algorithms to engineer RNA sequences, making it applicable in mRNA therapeutics and synthetic biology.

Fastprop is a Python package designed for fast molecular property prediction using deep-QSPR techniques. It automates the generation of molecular descriptors and trains a neural network to predict various molecular properties from SMILES strings.

PyCC is a Python-based implementation of the coupled cluster method used in quantum chemistry. It provides capabilities for calculating molecular energies and is designed for various quantum chemistry applications, including real-time simulations.

Lo-Hi Splitter is a tool designed for partitioning molecular datasets to facilitate drug discovery tasks such as lead optimization and hit identification. It employs methods to ensure that training and test sets are distinct, improving the predictive performance of models in drug discovery applications.

ChemFlow is a series of computational chemistry workflows designed to automate and simplify the drug discovery pipeline. It includes functionalities for docking, rescoring, and benchmarking, allowing users to focus on analysis and decision-making.

ProCyon is an open-source multimodal foundation model designed to predict protein phenotypes across various scales. It includes capabilities for drug-binding domain prediction and provides benchmarking models for systematic evaluation against other methods.

The 'cgdms' repository provides a Python package for differentiable molecular simulation of proteins using a coarse-grained potential. It includes functionalities for simulating protein dynamics, calculating energies, and designing proteins based on learned potentials.

PyFock is a pure Python Gaussian basis DFT code that enables efficient quantum chemistry calculations with GPU acceleration. It is designed for molecular systems and supports various functionalities for calculating molecular integrals and properties.

PackDock is a tool that implements a diffusion-based model for flexible protein-ligand docking, allowing for the effective packing of side chains in protein structures. It integrates with various ligand conformation sampling algorithms to enhance docking results.

This repository provides scripts and data for predicting protein stability using deep learning representations. It includes a model for rapid predictions and tools for data visualization and analysis, making it useful for researchers in molecular biology and computational chemistry.

This repository provides data and notebooks for fine-tuning protein language models to enhance predictions across diverse tasks. It includes training datasets and examples for generating embeddings and training models, making it a useful resource for molecular machine learning in protein-related applications.