/tools

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.

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.

SIRIUS is a software framework that facilitates the de-novo identification of metabolites using tandem mass spectrometry. It automates the analysis of mass spectra to deduce molecular formulas and structures, integrating various tools for enhanced metabolite identification.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Molchanica is a comprehensive tool for editing, visualizing, and simulating molecules and proteins. It includes features for molecular dynamics, docking, and predicting pharmacokinetic properties, making it suitable for drug discovery and molecular design.

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.

DockStream is a docking wrapper that automates docking execution and post hoc analysis, providing access to various ligand embedders and docking backends. It is integrated with the REINVENT platform, allowing for the incorporation of docking into the molecular design process.

MolRep is a Python library designed for deep representation learning aimed at predicting molecular properties. It includes a comprehensive evaluation of state-of-the-art models across multiple benchmark datasets, facilitating advancements in molecular property prediction.

Chemprop is a tool designed for guided molecular property prediction and discovery using evidential deep learning techniques. It enables uncertainty quantification in predictions, facilitating better optimization and virtual screening in drug discovery.