/tools

NBodySimulator.jl is a differentiable simulator designed for scientific machine learning, specifically for simulating N-body problems, including molecular dynamics. It allows users to model and visualize the interactions of multiple bodies, making it applicable in the field of molecular simulations.

ScanNet is an interpretable geometric deep learning model that predicts binding sites on proteins. It identifies functional sites such as protein-protein and protein-antibody interactions, leveraging the three-dimensional structure of proteins to enhance predictions.

FABind is a software tool designed for fast and accurate prediction of protein-ligand binding interactions. It includes enhancements for molecular docking through improved pocket prediction and pose generation, making it useful for drug discovery and molecular simulations.

This repository provides a conditional graph-based molecule generator designed for multi-objective de novo drug design. It allows users to generate molecules with controlled properties using a generative model, making it a valuable tool for drug discovery.

The RDKit Python Wheels repository provides precompiled binaries for the RDKit cheminformatics toolkit, which is used for various molecular modeling tasks including property prediction, molecular simulations, and manipulation of chemical structures. It facilitates the installation of RDKit, enabling users to perform computational chemistry and molecular biology tasks efficiently.

PANDORA is a research platform designed for high-dimensional data analysis in biomedical research, facilitating predictive modeling and biomarker discovery. It leverages advanced statistical methodologies to provide insights into systems biology and drug discovery.

DynaPhoPy is a software tool designed to calculate crystal microscopic anharmonic properties from molecular dynamics simulations. It utilizes normal-mode-decomposition techniques to analyze phonon frequency shifts and thermal properties, making it useful for researchers in materials science and molecular dynamics.

BFEE2 is a Python-based software designed for automating absolute binding free energy calculations using molecular dynamics simulations. It supports both alchemical and geometric routes for evaluating binding affinities in protein-ligand and protein-protein systems.

Meta-MGNN is a tool designed for predicting molecular properties using few-shot graph learning techniques. It includes datasets for training and evaluates performance on various molecular properties, making it relevant for computational chemistry applications.

This repository contains tutorials for using LAMMPS, a molecular dynamics simulation software. It offers step-by-step guides for beginners and advanced users to perform various molecular simulations, including studies on polymers, carbon nanotubes, and electrolyte systems.

Sella is a Python software package designed for saddle point optimization and minimization of atomic systems. It facilitates the identification of first order saddle points, which is crucial in molecular simulations and understanding reaction pathways.

MLatom is a Python package designed for atomistic simulations that integrates machine learning with quantum chemical methods. It supports various functionalities including molecular dynamics, geometry optimization, and the prediction of molecular properties using advanced AI models.

Tangelo is an open-source Python package that facilitates end-to-end chemistry workflows on quantum computers. It supports the design of quantum experiments and integrates with various quantum chemistry packages, making it suitable for molecular simulations and drug discovery applications.

GLN is a tool for predicting retrosynthesis pathways using a Conditional Graph Logic Network. It includes datasets for training and testing models, making it useful for molecular design and generation tasks.

Pallatom is a protein generation model that produces protein structures with all-atom coordinates by modeling the joint distribution of structure and sequence. It employs a novel network architecture to enhance designability, diversity, and novelty in protein design, making it a valuable tool for researchers in molecular biology.

SENPAI is a molecular dynamics simulation software that simplifies the process of simulating molecular systems for educational and academic purposes. It features an efficient computing model and user-friendly file formats, making it accessible for users on various devices.

CrystalFormer is a transformer-based autoregressive model that generates crystalline materials while considering space group symmetries. It utilizes reinforcement learning for fine-tuning and can produce stable crystal structures based on specified prototypes.

CIRpy is a Python interface that simplifies interaction with the NCI Chemical Identifier Resolver (CIR). It allows users to resolve chemical identifiers, such as names to SMILES strings, enhancing the accessibility of chemical data.

KarmaDock is a deep learning tool for ligand docking that enhances the speed and accuracy of predicting binding poses and affinities. It integrates advanced graph neural networks to model protein-ligand interactions and has been validated on benchmark datasets for drug discovery applications.

MLFF is a repository for training and developing machine learned force fields using the SO3krates transformer. It allows users to perform molecular dynamics simulations and evaluate molecular properties through trained models.

Uni-GBSA is an automatic workflow designed to perform MM/GB(PB)SA calculations for evaluating ligand binding free energies in virtual screening. It includes functionalities for topology preparation, structure optimization, and batch processing of multiple ligands against a protein target.

Dirichlet Flow Matching is a tool designed for DNA sequence generation and optimization, particularly in the context of enhancer and promoter design. It utilizes advanced machine learning techniques to facilitate molecular design tasks.

PPIFlow is a framework for the de novo generation of high-affinity biological binders, including antibodies and nanobodies. It integrates a design workflow that supports various tasks such as binder design, antibody design, and motif scaffolding, utilizing deep learning techniques for protein structure generation.