/tools

DFT_PIB_Code is a collection of interactive Jupyter Notebooks designed to teach the fundamentals of Density-Functional Theory (DFT) through visualizations and practical examples. It allows users to explore molecular properties and behaviors using DFT, making it a valuable educational resource in computational chemistry.

Quantum Package is an open-source suite of programs focused on determinant-driven quantum chemistry calculations. It provides tools for performing wave function methods, which are essential for predicting molecular properties and conducting simulations in computational chemistry.

BioSimSpace is a Python framework that facilitates biomolecular simulations and workflows, allowing users to create robust components that can operate across different software and hardware environments. It supports real-time interaction with molecular simulation processes, making it a valuable resource for computational chemistry and molecular biology applications.

Boltzina is a pipeline that integrates AutoDock Vina docking with Boltz-2 scoring to efficiently predict binding affinities for molecular docking. It supports both full docking and scoring-only modes, making it a valuable tool for virtual screening in drug discovery.

The Deep Tensor Neural Network (DTNN) is designed to provide insights into quantum-mechanical properties of molecular systems. It includes examples for training models to predict total energy, making it a useful tool for molecular property prediction.

Particle Guidance is a tool that enhances the diversity and efficiency of sampling in generative models, specifically applied to molecular conformer generation. It reduces the median error in conformer generation, making it a valuable resource for molecular design and optimization.

GPUMD-Tutorials provides various tutorials and examples for using the GPUMD package, which focuses on molecular dynamics simulations. The repository includes benchmark examples and tutorials for simulating properties of materials, such as thermal transport and phonon dynamics.

The simple-dftd3 repository provides a library implementation of the D3 dispersion correction model, which enhances the accuracy of density functional theory (DFT) calculations. It supports various programming interfaces, including Fortran, C, and Python, making it accessible for molecular simulations and property predictions.

The `kinisi` package is designed to quantify uncertainty in atomic and molecular displacements, enhancing the understanding of diffusion in materials through molecular dynamics simulations.

PXMeter is a toolkit for assessing the structural quality of biomolecular predictions, including proteins and small molecules. It provides multi-metric evaluations and supports both command line and Python API interfaces for efficient analysis.

The ppqm package facilitates the integration of RDKit with various quantum chemistry software, allowing users to perform molecular property calculations and optimizations. It serves as a bridge for cheminformatics applications, enabling efficient quantum chemistry computations in Python.

DeepDTAF is a deep learning architecture designed to predict the binding affinity between ligands and proteins. It integrates local and global features and provides a benchmark dataset for training and evaluation.

This tool implements a reinforced genetic algorithm for structure-based drug design, utilizing neural models to enhance the efficiency of molecular optimization. It aims to intelligently explore chemical space to identify potential drug candidates with improved binding affinity.

PySCF is a Python-based framework designed for quantum chemistry simulations. It provides tools for performing various quantum mechanical calculations, which are essential for predicting molecular properties and conducting simulations in computational chemistry.

The SHARC molecular dynamics program suite is an ab initio software package developed to study the excited-state dynamics of molecules. It provides tools for simulating molecular behavior and interactions at a quantum level.

DeepPBS is a tool that utilizes geometric deep learning techniques to predict the binding specificity between proteins and DNA. It allows for the processing and visualization of molecular interactions, making it relevant for studies in molecular biology and computational chemistry.

OpenMM Setup is an application designed to configure and run simulations with OpenMM. It offers a graphical interface for selecting input files and setting simulation options, allowing users to run simulations directly or save scripts for later execution.

The smina-docking-benchmark repository provides tools for evaluating molecular docking models and optimizing generated molecules. It includes benchmarks for various models and allows users to generate and assess molecules based on their docking scores.

MCMG is a software tool designed for generating molecules based on specific constraints using advanced machine learning techniques. It allows users to customize tasks for molecular generation, making it suitable for applications in drug discovery and molecular design.

GEMS is a graph-based deep learning model designed for predicting protein-ligand binding affinities. It integrates language model embeddings to enhance generalization and includes a refined dataset to minimize data leakage, making it a valuable resource for molecular property prediction.

SeqVec is a tool that creates embeddings for amino acid sequences using deep learning techniques. It enables the prediction of various protein properties and functions from single protein sequences, improving upon traditional methods that rely on evolutionary information.

CA RFdiffusion is a repository that provides training and inference code for a protein structure diffusion model. It generates protein backbones through a two-step process involving diffusion and refinement, making it a valuable tool for computational protein design.

This repository provides an open-source solution for conducting computational studies in drug discovery using both quantum and classical computing resources. It includes sample code for various drug discovery problems, such as molecular docking and protein folding, facilitating research in these areas.

LitMatter is a template designed for rapid experimentation and scaling of deep learning models on molecular and crystal graphs. It supports various applications in drug discovery and molecular dynamics, allowing researchers to efficiently train models for predicting molecular properties and simulating molecular interactions.