/tools

AF3Score is a pipeline for evaluating the quality of protein structures using metrics derived from AlphaFold3. It generates various scoring metrics to assess the accuracy of protein models, focusing on both global and local structural features.

MoleculeNet is a collection of datasets and benchmarks designed for evaluating machine learning models in the context of molecular property prediction. It includes various datasets relevant to physical chemistry, biophysics, and materials science, facilitating research in drug discovery and molecular modeling.

CellListMap.jl is a Julia package designed for efficient computation of interactions and properties based on pairwise distances between particles in molecular dynamics simulations. It supports both parallel and serial implementations and can handle periodic boundary conditions.

PEER Benchmark is a comprehensive tool designed for evaluating various methods in protein sequence understanding. It includes multiple tasks such as protein function prediction and protein-ligand interaction prediction, facilitating advancements in molecular biology research.

Evolocity is a Python package that utilizes evolutionary velocity to analyze protein evolution through language models. It provides tools for predicting evolutionary dynamics and includes benchmarking scripts for performance evaluation.

FoldBench is a benchmarking tool for all-atom biomolecular structure prediction, enabling the evaluation of models across various biomolecular interactions and monomeric structures. It includes a comprehensive dataset for proteins, nucleic acids, and ligands, facilitating assessments of prediction accuracy in molecular simulations.

The Benchmarking-Single-Cell-Perturbation repository provides a library of models for analyzing genetic and chemical perturbation data in single-cell contexts. It includes tools for predicting cellular responses to perturbations and offers datasets and benchmarks for evaluating model performance in drug discovery applications.

DiffAbXL is a benchmarking tool for generative models aimed at antibody design, specifically evaluating log-likelihood scores for ranking antibody sequences based on their predicted binding affinities. It provides datasets and methodologies for assessing the performance of various models in the context of antibody optimization.

MLIP Arena is a benchmark platform designed to evaluate machine learning interatomic potentials (MLIPs) beyond conventional error metrics. It aims to improve the predictive accuracy and efficiency of molecular modeling by assessing the physical soundness of MLIPs in real-world scenarios.

PaRoutes is a framework that benchmarks multi-step retrosynthesis methods, providing curated datasets for building retrosynthesis models. It includes scripts for analyzing route quality and diversity, making it a valuable resource for researchers in molecular design.

This repository provides a benchmark dataset of proteins and ligands for validating binding free energy prediction methods. It includes input structures for calculations, experimental binding data for reproducibility studies, and analysis scripts for generating results.

MDBenchmark is a tool that facilitates the generation, execution, and analysis of benchmarks for molecular dynamics simulations. It optimizes the use of computing resources by allowing users to compare performance across different simulation setups and visualize the results.

GPUMD-Tutorials provides various tutorials and examples for using the GPUMD package, which focuses on molecular dynamics simulations. The repository includes benchmark examples and tutorials for simulating properties of materials, such as thermal transport and phonon dynamics.

PXMeter is a toolkit for assessing the structural quality of biomolecular predictions, including proteins and small molecules. It provides multi-metric evaluations and supports both command line and Python API interfaces for efficient analysis.

The smina-docking-benchmark repository provides tools for evaluating molecular docking models and optimizing generated molecules. It includes benchmarks for various models and allows users to generate and assess molecules based on their docking scores.

FastMDAnalysis is a Python-based software tool that automates the analysis of molecular dynamics trajectories, allowing users to perform various analyses such as RMSD, RMSF, and clustering with simple commands. It is designed to streamline workflows and generate publication-quality figures, making it suitable for researchers in molecular dynamics.

This repository explores evolution-aware protein language models to predict protein functions. It provides datasets related to metal ion binding and antibiotic resistance, making it a valuable resource for researchers in molecular biology and protein analysis.

RNA3DB is a dataset of non-redundant RNA structures from the PDB, designed for training and benchmarking deep learning models focused on RNA structure prediction. It includes various RNA chains labeled with non-coding RNA families and provides tools for customizing and building the dataset.

LP-PDBBind is a repository that develops scoring functions using the PDBBind dataset, providing tools for dataset creation and model retraining for predicting molecular properties such as binding affinities. It includes compiled datasets and scripts for preparing and analyzing protein-ligand complexes.

Lo-Hi Splitter is a tool designed for partitioning molecular datasets to facilitate drug discovery tasks such as lead optimization and hit identification. It employs methods to ensure that training and test sets are distinct, improving the predictive performance of models in drug discovery applications.

ChemFlow is a series of computational chemistry workflows designed to automate and simplify the drug discovery pipeline. It includes functionalities for docking, rescoring, and benchmarking, allowing users to focus on analysis and decision-making.

ProCyon is an open-source multimodal foundation model designed to predict protein phenotypes across various scales. It includes capabilities for drug-binding domain prediction and provides benchmarking models for systematic evaluation against other methods.

The SAMPL6 repository contains challenge inputs and results for predicting molecular properties, specifically focusing on pKa and logP values of small molecules. It serves as a benchmark for evaluating computational methods in predicting these properties, providing datasets and performance evaluations for participants.

Uni-FEP-Benchmarks is a benchmark dataset aimed at systematically evaluating the Uni-FEP method for binding free energy calculations. It compiles diverse protein-ligand systems and chemical transformations to facilitate the validation and optimization of the Uni-FEP methodology, contributing to advancements in drug discovery.