/tools

Vermouth and Martinize2 are tools for generating coarse-grained structures and topologies from atomistic molecular structures. They utilize graph algorithms to describe and apply transformations, primarily aimed at enhancing molecular dynamics simulations.

The `graph-pes` framework is designed to accelerate the development of machine-learned potential energy surface models that utilize graph representations of atomic structures. It allows researchers to train, fine-tune, and deploy models for molecular dynamics simulations and energy calculations.

The 'geometric-gnns' repository provides a curated list of Geometric Graph Neural Networks designed for 3D atomic systems. It includes various models, their characteristics, and a collection of datasets, facilitating research in molecular property prediction and simulations.

ProtST is a pretraining framework designed for understanding and predicting protein sequences by integrating protein functions with biomedical texts. It supports various downstream tasks such as protein localization prediction and zero-shot protein classification, enhancing the capabilities of protein language models.

ScoreMD is a framework designed for training energy-based diffusion models that can perform both independent sampling and continuous molecular dynamics simulations. It provides tools for energy estimation and generative modeling in the context of molecular dynamics.

LLaMP is a multimodal retrieval-augmented generation framework designed to enhance large language models with high-fidelity materials knowledge. It allows for dynamic interaction with materials databases, making it useful for applications in materials informatics and molecular property prediction.

suanPan is an open-source finite element analysis framework designed for efficient simulations in solid mechanics and engineering applications. It supports parallel and heterogeneous computing, making it suitable for complex simulations, including those in molecular dynamics.

RetroExplainer is a deep-learning framework designed for predicting retrosynthesis pathways with a focus on molecular assembly reasoning and interpretability. It allows users to generate and analyze potential synthetic routes for chemical compounds, making it a useful tool in the field of molecular design.

Str2Str is a framework designed for zero-shot protein conformation sampling, utilizing a score-based approach to perturb protein structures and sample conformations. It integrates with existing tools for molecular dynamics simulations and protein folding, making it a valuable resource for computational biology.

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.

PAMNet is a universal framework designed for accurate and efficient geometric deep learning of molecular systems. It excels in predicting molecular properties, such as binding affinities and RNA 3D structures, and utilizes graph neural networks to enhance performance in these tasks.

Faunus is a molecular simulation package designed for Metropolis Monte Carlo simulations of molecular systems. It supports various statistical mechanical ensembles and includes features for free energy sampling and parallel tempering.

MatterTune is a machine learning library that allows computational chemists and materials scientists to fine-tune pre-trained models for various molecular properties such as energy and forces. It supports multiple dataset formats and provides tools for training and optimizing atomistic models.

Maize is a graph-based workflow manager that allows users to define and execute complex computational chemistry pipelines. It supports arbitrary graph topologies and task dependencies, making it suitable for various molecular applications.

GeminiMol is a molecular representation model that enhances molecular feature extraction by incorporating conformational space profiles. It is designed for applications in drug discovery, including virtual screening, target identification, and quantitative structure-activity relationship (QSAR) modeling.

Sire is a molecular modeling framework designed for biomolecular simulations, enabling users to prototype algorithms and exchange information between various molecular simulation programs. It supports functionalities such as molecular dynamics, parameterization, and trajectory analysis, making it a versatile tool for researchers in computational chemistry and molecular biology.

HalluDesign is an all-atom framework that utilizes a structure prediction model to iteratively co-optimize and co-design protein sequences and structures. It allows for the design of new protein sequences based on structural hallucination, making it a valuable tool in molecular biology and protein engineering.

The Otter Knowledge repository enhances protein sequence and SMILES drug databases with a multimodal knowledge graph, improving predictions on drug-target binding affinity benchmarks. It provides pre-trained models and datasets for representation learning in drug discovery.

pyCOFBuilder is a Python package designed for the automated assembly of Covalent Organic Framework structures using a reticular approach. It allows users to generate a variety of COF structures based on specified building blocks and functional groups, facilitating high-throughput molecular design.

The METL framework provides tools for pretraining and finetuning biophysics-informed protein language models, enabling users to train models on mutational data and generate predictions. It includes datasets for training and examples for running inference, making it a valuable resource for protein engineering and design.

Wepy is a modular framework for conducting weighted ensemble simulations in Python, designed to facilitate molecular dynamics and enhance sampling methods. It supports integration with OpenMM for molecular simulations and aims to provide a user-friendly interface for various simulation tasks.

The OpenFF Evaluator is a toolkit developed by the Open Forcefield Consortium for the automated estimation of physical property datasets derived from molecular simulations. It provides a scalable framework for evaluating various molecular properties, making it a valuable resource for computational chemistry and molecular biology applications.

OpenFermion-ProjectQ is a plugin that allows for circuit simulation and compilation in quantum computing, specifically targeting fermionic systems. It interfaces with OpenFermion to facilitate the analysis and compilation of quantum algorithms related to electronic structure.

Lemon is a framework that allows users to rapidly mine structural information from the Protein Data Bank. It enables the creation of standardized workflows for querying 3D features of macromolecules, enhancing the efficiency of structural biology research.