/tools

Kappa is a domain-specific language that facilitates the modeling of protein interactions, enabling users to build executable models for systems biology. It includes tools for both simulation and static analysis of these interactions.

ADFLIP is a tool for all-atom inverse protein folding using discrete flow matching techniques. It allows users to sample protein sequences from input files, facilitating the design and prediction of protein structures.

RetroCast is a comprehensive toolkit designed for standardizing, scoring, and analyzing multistep retrosynthesis models. It provides a unified framework for evaluating different retrosynthesis algorithms, facilitating rigorous comparisons and improving reproducibility in molecular design.

Joltz is a translation of the boltz model from PyTorch to JAX, primarily aimed at protein design through a method called hallucination. It is compatible with JAX's features and is intended for use in molecular design applications.

ELASPIC2 is a tool designed to predict the effects of mutations on protein stability and binding affinity using pretrained neural networks. It provides various interfaces, including a web server, Python API, and command-line interface, making it accessible for users to analyze protein mutations effectively.

BagPype is a Python package designed for constructing atomistic, energy-weighted graphs from biomolecular structures. It allows for detailed modeling of molecular properties by incorporating various types of interactions, making it useful for studying proteins and nucleic acids.

`augment-atoms` is a tool designed for augmenting datasets of atomic configurations using a model-driven approach. It generates new molecular structures by applying transformations to existing ones, making it useful for enhancing datasets in molecular machine learning applications.

Modelforge is a package designed to implement and train neural network potentials (NNPs) for molecular simulations. It includes infrastructure for optimizing and storing these models, along with datasets for accurate training and validation.

This repository provides a method to run AlphaFold in batch mode using multiprocessing, allowing for efficient prediction of protein structures. It is designed to handle multiple structures simultaneously, enhancing the usability of AlphaFold for large-scale protein design tasks.

The 'molgrad' repository contains code for using explainable AI to assess molecular properties relevant to drug discovery. It includes pre-trained models for predicting plasma protein binding and other properties, as well as functionality for training custom models and generating explanations for molecular features.

eminus is a Python-based code for electronic structure theory that implements plane wave density functional theory (DFT) with self-interaction correction functionalities. It aims to provide an accessible and extensible framework for performing quantum chemical calculations and simulations.

This repository provides parsers for a variety of electronic codes, enabling the processing of simulation data in molecular dynamics and quantum chemistry. It facilitates the integration of simulation results into the NOMAD database for further analysis and research.

PyMissense is a tool that generates pathogenicity plots and modified PDB files for custom proteins based on the AlphaMissense paper. It helps identify critical regions in amino acid chains that affect protein function and visualizes pathogenicity predictions.

ComplexMixtures.jl is a Julia package designed to study the interactions between solutes and solvents of complex molecular shapes. It utilizes minimum-distance distribution functions to analyze these interactions, providing insights into molecular behavior in solutions.

GMXAnalysis is a repository that provides scripts for post-processing molecular dynamics simulations conducted with GROMACS. It includes tools for generating secondary structure maps, calculating binding energies, and converting file formats, making it useful for molecular analysis.

ProteinEnsembles.jl is a Julia package that implements the ExProSE algorithm to generate and perturb ensembles of protein structures. It allows for the prediction of allosteric sites and includes various functions for structural bioinformatics, making it a valuable tool for molecular biology research.

Equivariant-Graph-Transformer is a deep neural network that combines an Equivariant Graph Neural Network with a Transformer-Encoder to predict molecular potentials. It provides a complete workflow for data preparation, model training, and evaluation, making it a valuable tool for molecular property prediction.

The 'vina_docking' repository contains Python scripts for performing molecular docking using AutoDock Vina. It allows users to preprocess receptor and ligand files, run docking simulations, and output results, making it a valuable tool for drug discovery and molecular interactions.

This repository implements a method that combines pre-trained protein language models with geometric deep learning networks to enhance the representation of macromolecules. It addresses tasks such as binding affinity prediction and protein-protein interface prediction, making it a valuable tool in molecular biology and computational chemistry.

ProCALM is a protein conditionally adapted language model designed for the generation of enzymes based on specific conditions such as EC number and taxonomy. It utilizes autoregressive transformers to create functional sequences, making it a valuable tool for protein design and molecular generation.

This repository contains files and resources for a workshop series on quantum chemistry calculations using Python. It covers topics such as molecule input generation and the basics of DFT, along with methods to derive molecular properties from quantum mechanical calculations.

QCxMS2 is a program package that calculates electron ionization mass spectra using quantum mechanical methods. It automates the exploration of reaction networks, making it a useful tool for analyzing molecular properties in computational chemistry.

FieldSchNet is a deep learning framework that models the interaction of molecules with external fields, enabling the prediction of molecular properties such as energies and spectra. It supports molecular dynamics simulations and can be used in QM/MM setups for advanced molecular modeling.