/tools

ElectronVisualized is a web-based interactive tool that visualizes atomic and molecular orbitals in quantum mechanics. It allows users to draw molecules and explore their properties, making it a useful resource for those studying chemistry and molecular structures.

The ChEMBL MCP Server is a comprehensive tool that facilitates drug discovery and cheminformatics analysis by providing access to the ChEMBL chemical database. It includes features for searching compounds, analyzing bioactivity data, and predicting molecular properties such as ADMET and solubility.

AiCE is a tool for predicting high-fitness mutations in proteins by utilizing inverse folding models that incorporate structural and evolutionary constraints. It allows for single and multi-mutation nominations, making it relevant for protein engineering and design applications.

ReaxTools is a high-performance post-processing code designed for analyzing ReaxFF and AIMD trajectories using graph theory. It facilitates the analysis of molecular dynamics simulations, making it a valuable tool for researchers in computational chemistry.

This tool provides a framework for simulating time-integrated coarse-grained molecular dynamics using multi-scale graph neural networks. It includes datasets for training and evaluation, making it suitable for molecular dynamics applications.

This repository provides a method for predicting the subcellular location of proteins using transformer protein embeddings and a linear attention mechanism. It includes tools for training models and making predictions based on protein sequences.

BALL is a Biochemical Algorithms Library that provides tools for molecular simulations, cheminformatics, and drug design. It supports various biochemical computations and is useful for researchers in computational biology and chemistry.

LaMBO is a tool that accelerates Bayesian optimization for biological sequence design using denoising autoencoders. It allows for efficient exploration of protein sequences to optimize properties such as stability and surface area, outperforming traditional genetic algorithms in its evaluations.

The ESMDiff repository provides a framework for generating protein conformations using structure language models. It employs a discrete variational auto-encoder and conditional language modeling to capture conformational distributions, making it a valuable tool for protein design and molecular dynamics.

Chemeleon is a text-guided diffusion model designed for generating crystal structures based on natural language descriptions or specified chemical compositions. It aids in material discovery by allowing users to explore and create crystal structures, making it a valuable tool in computational chemistry.

OpenMM PLUMED Plugin allows users to connect OpenMM simulations with PLUMED for enhanced analysis and biasing based on collective variables. It facilitates advanced molecular dynamics simulations, making it a valuable tool in computational chemistry.

The _grand_ module enables the simulation of grand canonical Monte Carlo (GCMC) insertions and deletions of water molecules in OpenMM. This tool enhances the sampling of water molecules in binding sites, making it useful for molecular dynamics studies.

S-PLM is a structure-aware protein language model that utilizes contrastive learning to integrate sequence and structural information for generating protein embeddings. It supports downstream tasks such as enzyme classification and protein structure prediction, making it a valuable tool in molecular biology.

BO-ICL is a tool that utilizes Bayesian optimization and in-context learning to predict molecular properties and generate new molecular data. It allows users to perform regression with uncertainties and supports inverse design for proposing new molecular structures based on desired properties.

MXMNet is a molecular mechanics-driven graph neural network that utilizes multiplex graphs to analyze molecular structures. It is designed to predict various molecular properties and has applications in drug discovery.

gnina-torch is a PyTorch implementation of the GNINA scoring function, designed for molecular docking applications. It allows users to train and utilize deep learning models to predict protein-ligand interactions, enhancing the drug discovery process.

Dyna-1 is a model designed to predict micro-millisecond motions in proteins using their sequence and structure. It includes curated datasets for evaluation and is aimed at advancing the understanding of protein dynamics through deep learning techniques.

This repository contains OPLS-AA force field parameters for ionic liquids, enabling accurate molecular simulations. It includes both unscaled and scaled parameters, along with a tutorial for constructing and simulating ionic liquid systems using GROMACS.

pepp'r is a package designed for the evaluation of predicted molecular poses, allowing users to compute various metrics to assess the quality of structure predictions. It supports a wide range of metrics applicable to small molecules, proteins, and nucleic acid complexes.

This repository provides notebooks and an environment for a workshop on Python scripting for molecular docking. It includes resources for preparing and executing docking simulations, specifically targeting small molecules and proteins.

PCMol is a multi-target de novo molecular generator that utilizes AlphaFold's protein embeddings to create relevant molecules for various protein targets. It is designed to aid in drug discovery by generating SMILES representations of potential drug candidates.

pwtools is a Python package designed for pre- and postprocessing of atomistic calculations, primarily focused on software like Quantum Espresso, CPMD, CP2K, and LAMMPS. It includes powerful parsers and data types for managing calculation data, facilitating molecular simulations and analyses.

The CL&P force field repository offers a set of parameters for simulating ionic liquids using molecular dynamics software like LAMMPS, DL_POLY, and GROMACS. It includes input files and tools for building molecular configurations, making it a valuable resource for researchers in computational chemistry and materials science.

HuDiff is a tool designed for the humanization of antibodies and nanobodies using diffusion models. It includes training and evaluation scripts, as well as preprocessed datasets for developing humanized antibodies from mouse antibodies.