/tools

The qca-dataset-submission repository contains scripts for generating and submitting datasets to the QCArchive, facilitating the lifecycle of molecular data submissions. It supports the creation of datasets that can be used for various molecular property predictions and computational chemistry tasks.

GIT-Mol is a multi-modal large language model that integrates graph, image, and text data to perform various molecular tasks, including property prediction and molecule generation. It utilizes a novel architecture called GIT-Former to map different modalities into a unified latent space, facilitating advanced molecular analysis and generation.

The KIM API is a system-level library designed to facilitate the development of atomistic and molecular simulation programs by providing a standardized interface for various interatomic models. It supports multiple programming languages, enabling seamless integration into existing simulation workflows.

The 'alphafold_singularity' repository contains a Singularity recipe for running AlphaFold, a software that predicts protein structures. It facilitates the use of AlphaFold in high-performance computing environments, enabling researchers to perform protein folding simulations efficiently.

qcmath is a collection of Mathematica modules designed to facilitate electronic structure calculations in quantum chemistry. It aims to provide a user-friendly platform for newcomers to develop their ideas and perform various quantum chemistry methods, including Hartree-Fock and Møller-Plesset perturbation theory.

VisualDynamics is a web platform developed with Python and NextJS that automates GROMACS simulations. It facilitates molecular dynamics simulations, making it easier for researchers to conduct complex simulations in computational chemistry.

BALM is a deep learning framework designed to predict binding affinities between proteins and ligands by fine-tuning pretrained language models. It utilizes the BindingDB dataset and proposes improved evaluation strategies for assessing model performance, making it a practical tool for early-stage drug discovery screening.

This repository contains code for predicting properties of quantum systems using conditional generative models. It includes tools for generating datasets, training models, and simulating quantum systems, making it relevant for molecular property prediction and simulation tasks.

Uni-MOF is a transformer-based framework designed for high-accuracy predictions of gas adsorption in metal-organic frameworks (MOFs). It utilizes a large dataset of MOF structures to learn representations and predict various properties, making it a valuable tool in computational chemistry and materials science.

UVVisML is a tool designed to predict the optical properties of molecules using machine learning techniques. It provides functionalities for making predictions based on experimental and theoretical data, specifically targeting absorption peaks and excitation energies.

ReTReK is a data-driven application designed for retrosynthesis planning, leveraging knowledge from US patent datasets to predict synthetic routes for target molecules. It aims to assist chemists in designing efficient synthesis pathways using various scoring metrics to evaluate potential reactions.

The 'torsiondrive' repository provides a dihedral scanner that utilizes wavefront propagation techniques. It is designed to assist in the analysis and optimization of molecular structures, which is essential in computational chemistry and molecular design.

gcWGAN is a tool developed for de novo protein design using a guided conditional Wasserstein GAN. It leverages deep generative models to explore sequence-structure relationships and generate novel protein sequences based on given folds.

ChemIQ is a benchmark for evaluating the chemical intelligence of large language models by testing their ability to interpret molecular structures and perform chemical reasoning tasks. It includes a variety of questions related to molecular properties and transformations, making it relevant to the field of computational chemistry.

RGMolSA is a tool for ligand-based virtual screening that utilizes a new surface-based molecular shape descriptor derived from Riemannian geometry. It aims to predict potential new hits by comparing molecular shapes to those with known favorable properties, facilitating the drug discovery process.

PocketAnchor is a tool designed for learning structure-based pocket representations to predict protein-ligand interactions. It includes functionalities for predicting binding sites and affinities, making it relevant for molecular property prediction in drug discovery.

ConFit is a machine learning method that utilizes protein language models to learn the fitness landscape of proteins with limited experimental data. It employs a contrastive learning strategy to enhance predictions of protein-specific fitness, making it useful for applications in protein design and optimization.

Mjolnir is a flexible and efficient coarse-grained molecular dynamics simulation package that supports various simulation algorithms and interactions. It is designed to facilitate the implementation of new forcefields and is suitable for simulating proteins and DNA.

This repository contains algorithms for automatically fragmenting molecules into specified molecular subunits, such as functional groups. It aims to enhance the understanding and manipulation of molecular structures in computational chemistry.

The GenUI framework offers a backend for molecular generation and QSAR modeling through a REST API, enabling users to manage datasets and visualize chemical spaces. It supports the integration of various molecular generators and facilitates the upload and download of bioactivity datasets.

FAHBench is the official Folding@Home benchmark that utilizes the OpenMM molecular dynamics engine to evaluate performance on various OpenCL and CUDA-capable devices. It is designed to facilitate research in protein dynamics by providing a standardized benchmarking framework.

UFEDMM is a Python package that extends OpenMM for efficient simulations in extended phase spaces, enabling enhanced sampling and accurate free energy calculations. It facilitates the study of molecular systems by allowing users to compute free energy surfaces and perform simulations that overcome free-energy barriers.

SEEKR2 is a tool designed for multiscale milestoning to compute molecular thermodynamics and kinetics. It enables users to prepare and run simulations using various molecular dynamics engines to analyze processes such as ligand binding and membrane permeability.