/tools

Biomni is a general-purpose biomedical AI agent that enhances research productivity by integrating large language model reasoning with planning and execution. It can predict molecular properties, generate hypotheses, and evaluate biological reasoning tasks, making it a versatile tool in the biomedical field.

This repository is a curated list of papers that explore the use of deep learning techniques in protein design. It includes resources on benchmarks and datasets relevant to the field, making it a valuable tool for researchers in computational biology.

Protenix is an open-source tool designed for high-accuracy biomolecular structure prediction, particularly for proteins. It includes related projects for protein design and benchmarking, enhancing its utility in computational biology and drug discovery.

The Therapeutics Data Commons (TDC) is an open-source initiative that facilitates the development and evaluation of AI methods for drug discovery. It offers ready-to-use datasets, benchmarks for model comparison, and tools for predicting molecular properties and generating new biomedical entities.

CP2K is a quantum chemistry and solid state physics software package that enables atomistic simulations of various systems, including molecular and biological ones. It supports multiple modeling methods and can perform simulations such as molecular dynamics, energy minimization, and transition state optimization.

MOSES is a benchmarking platform for molecular generation models that facilitates research in drug discovery by providing datasets and metrics to evaluate the quality and diversity of generated molecules. It implements various generative models and standardizes the evaluation process for molecular generation.

This repository provides a comprehensive list of papers and resources related to molecular and material design using generative AI and deep learning techniques. It covers various methodologies for drug design, molecular optimization, and includes datasets and benchmarks relevant to the field.

AIRS is an open-source collection of software tools and datasets focused on artificial intelligence applications in quantum, atomistic, and continuum systems. It includes resources for predicting molecular properties, designing molecules, and conducting simulations, making it highly relevant to the fields of computational chemistry and molecular biology.

OpenFold3-preview is an open-source biomolecular structure prediction model that aims to replicate the capabilities of AlphaFold3. It supports the prediction of structures for proteins, RNA, and DNA, and includes benchmarking against state-of-the-art models.

DiffSBDD is an implementation of an equivariant diffusion model aimed at structure-based drug design. It allows users to generate new ligands, optimize existing molecules, and benchmark performance using various datasets, making it a comprehensive tool for molecular design and analysis.

HOOMD-blue is a Python package that facilitates molecular dynamics and Monte Carlo simulations of particle systems on CPUs and GPUs. It supports a variety of particle interactions and is particularly aimed at the soft matter research community.

RFdiffusion2 is an open-source tool designed for enzyme design and molecular generation using advanced inference techniques. It supports the creation of protein structures from atomic motifs and includes benchmarking capabilities for evaluating design performance.

ProteinGym is a comprehensive repository of benchmarks for Deep Mutational Scanning (DMS) assays, allowing for the evaluation of various mutation effect predictors. It includes extensive datasets of clinical variants and experimental measurements, facilitating research in protein design and mutation effect prediction.

PoseBusters is a tool designed to perform plausibility checks on generated molecule poses, particularly in the context of docking studies. It allows users to validate the accuracy of predicted ligand poses against known protein-ligand complexes, contributing to the assessment of molecular docking methods.

CBGBench is a benchmark tool for generative target-aware molecule design, integrating multiple state-of-the-art methods for generating molecules. It supports tasks such as linker design, fragment growing, and scaffold hopping, making it a valuable resource for researchers in drug discovery.

OpenMMTools is a Python library that enhances the OpenMM molecular simulation engine by providing tools for various molecular dynamics tasks, including free energy calculations and Markov chain Monte Carlo methods. It is designed to facilitate the development of comprehensive molecular simulation packages.

Uni-Dock is a GPU-accelerated molecular docking program designed to enhance the speed and accuracy of virtual screening processes. It supports various scoring functions and includes tools for handling input and output, as well as benchmarks for evaluating performance against public datasets.

PLINDER is a dataset and evaluation resource focused on protein-ligand interactions, containing over 400k systems and numerous annotations for training and benchmarking docking algorithms. It aims to standardize the evaluation of protein-ligand interactions in the field of computational chemistry.

ProteinWorkshop is a benchmarking framework designed for protein representation learning. It includes a variety of pre-training and downstream task datasets, models, and utilities, making it a valuable resource for researchers in molecular biology and computational chemistry.

ASE_ANI is a prototype interface for the ANI-1x and ANI-1ccx neural network potentials, enabling predictions of molecular properties and facilitating molecular dynamics simulations. It is designed for use within the Atomic Simulation Environment (ASE) and supports various applications in computational chemistry.

nablaDFT is a comprehensive dataset and benchmark designed for evaluating neural network potentials in molecular property prediction and Hamiltonian prediction. It includes a large collection of drug-like molecules with calculated electronic properties, making it a valuable resource for computational chemistry and machine learning applications in drug discovery.

MolScore is an automated scoring function that facilitates and standardizes the evaluation of generative models for de novo molecular design. It allows users to implement multi-parameter objectives for drug design, benchmark generative models, and evaluate generated molecules using various metrics.

The 'protein-ligand-benchmark' repository offers a comprehensive dataset designed for testing parameters and methods of free energy calculations in protein-ligand interactions. It includes detailed metadata for various protein targets and ligands, facilitating research in molecular property prediction and computational chemistry.

Matbench Discovery is an evaluation framework that ranks machine learning models on tasks related to high-throughput discovery of stable inorganic crystals. It predicts material properties such as thermodynamic stability and thermal conductivity, providing insights for building large-scale materials databases.