/tools

MMseqs2 is an ultra-fast and sensitive software suite for searching and clustering large sets of protein and nucleotide sequences. It significantly improves the speed and sensitivity of sequence searches compared to traditional methods like BLAST, making it a valuable tool for molecular biology research.

Chai-1 is a multi-modal foundation model for predicting the structures of various biomolecules, including proteins, small molecules, DNA, and RNA. It utilizes advanced techniques to achieve state-of-the-art performance in molecular structure prediction across multiple benchmarks.

AlphaGenome is an API that offers access to a model for predicting various functional outputs from DNA sequences, including gene expression and variant effects. It is designed for analyzing genomic data and provides tools for visualization and variant scoring.

RoseTTAFold All-Atom is a neural network designed for predicting the structures of various biomolecular assemblies, including proteins, nucleic acids, and small molecules. It provides functionalities for predicting protein-nucleic acid complexes and covalently modified proteins, making it a valuable tool in molecular biology and computational chemistry.

Poly is a Go package designed for engineering organisms, providing tools for tasks such as codon optimization and primer design. It aims to be a comprehensive resource for computational synthetic biology, making it useful for both academic and industrial applications.

DNA Features Viewer is a Python library designed to visualize DNA sequence features from GenBank or GFF files. It allows users to create clear plots of DNA sequences, making it useful for synthetic biology applications and DNA design.

PyRosetta.notebooks offers Jupyter Notebooks that serve as a learning resource for the PyRosetta platform, which is used for biomolecular structure prediction and design. The repository includes tutorials on protein folding, docking, and design, making it a valuable tool for researchers in computational biology and chemistry.

Awesome Flow Matching is a collection of works focused on flow matching and stochastic interpolants, which are relevant for generative modeling in various domains, including molecular design. It includes applications for generating and optimizing molecular structures, particularly in the context of DNA sequence design.

AlphaGenome is a unified DNA sequence model that predicts regulatory variant effects and provides insights into genome function. It analyzes DNA sequences to deliver predictions at single base-pair resolution across various modalities, including gene expression and splicing patterns.

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.

ProLIF (Protein-Ligand Interaction Fingerprints) is a tool that generates interaction fingerprints for complexes involving ligands, proteins, DNA, or RNA. It utilizes data from molecular dynamics trajectories and docking simulations, making it valuable for drug discovery and cheminformatics.

LightDock is a docking framework that utilizes the Glowworm Swarm Optimization algorithm to facilitate the docking of proteins, peptides, and DNA. It allows users to define custom scoring functions and apply residue restraints, making it a versatile tool for studying molecular interactions.

gReLU is a Python library designed for training, interpreting, and applying deep learning models to DNA sequences. It provides functionalities for genome annotation and processing, making it a useful tool in the field of molecular biology.

Mol-Instructions is a dataset that contains a large collection of instructions for biomolecular tasks, including molecule-oriented and protein-oriented tasks. It aims to facilitate the development of large language models for generating and understanding molecular and protein-related information.

ESPResSo is a versatile molecular dynamics software package that enables the simulation and analysis of many-particle systems using coarse-grained models. It is applicable in various fields including physics, chemistry, and molecular biology, allowing for the study of polymers, colloids, and biological systems.

ATOMICA is a geometric AI model that learns universal representations of intermolecular interactions at an atomic scale. It is pretrained on a large dataset of molecular interaction interfaces and can be used for various downstream tasks, including binding site prediction and embedding biomolecular complexes.

CodonTransformer is a deep learning tool for optimizing DNA sequences to enhance protein expression in various organisms. It leverages a large dataset of DNA-protein pairs to predict and generate host-specific DNA sequences with optimized codon usage.

ModelAngelo is an automatic atomic model building program designed for cryo-electron microscopy (cryo-EM) maps. It allows users to build models of proteins and nucleic acids from cryo-EM data, facilitating the identification and modeling of molecular structures.

PENSA is a Python library designed for the exploratory analysis and comparison of biomolecular conformational ensembles, particularly from molecular dynamics simulations. It offers functionalities such as structural ensemble comparison, clustering, and trajectory processing, making it a valuable tool for researchers in molecular biology and biophysics.

CRISPR-GPT is a Large Language Model agent designed to automate and streamline gene-editing experiments, particularly using CRISPR technology. It assists researchers in planning, executing, and analyzing various gene-editing tasks, enhancing efficiency and accuracy in molecular biology applications.

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.

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.

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.