/tools

Uni-Fold is an open-source platform that advances protein modeling beyond AlphaFold, enabling accurate predictions of protein structures, including monomers and multimers. It provides tools for training and inference, making it a valuable resource for researchers in molecular biology and computational chemistry.

IgFold is a tool for fast and accurate prediction of antibody structures from sequences using deep learning techniques. It supports refinement methods and provides embeddings for machine learning applications, making it relevant for molecular design and representation.

AlphaFold - PyTorch is an implementation of DeepMind's AlphaFold for predicting protein structures using deep learning techniques. It allows researchers to generate accurate models of protein folding, which is crucial for understanding biological functions and drug design.

The Rosetta Bio-macromolecule modeling package is a comprehensive suite for computational modeling and analysis of protein structures. It includes algorithms for de novo protein design, enzyme design, and ligand docking, facilitating significant advancements in computational biology.

The 'rgn' repository provides a TensorFlow implementation of recurrent geometric networks for end-to-end differentiable learning of protein structures. It allows users to train models and predict protein structures from sequences, making it a valuable tool in the field of molecular biology and computational chemistry.

The SE3-Transformer-PyTorch repository provides an implementation of SE3-Transformers for equivariant self-attention, specifically aimed at applications in protein structure prediction and drug discovery. It allows for the modeling of molecular interactions and features, making it a valuable tool in computational chemistry and molecular biology.

Equiformer is a PyTorch implementation of an SE3/E3 equivariant attention network that achieves state-of-the-art results in protein folding. It utilizes advanced techniques in deep learning to model molecular interactions and predict protein structures.

OpenComplex is an open-source platform designed for developing protein and RNA complex models, leveraging features from AlphaFold 2 and OpenFold. It allows for high-precision modeling and inference of RNA and protein-RNA complexes, making it a valuable tool in computational biology.

The 'mosaic' repository provides a framework for functional, multi-objective protein design using continuous relaxation and machine learning models. It allows users to combine various predictors to optimize protein properties such as binding affinity and solubility, making it a valuable tool for computational biology and molecular design.

Ankh is an optimized protein language model that enhances general-purpose modeling for protein engineering. It offers pre-trained models and datasets for various protein-related tasks, including secondary structure prediction and solubility assessment.

This repository offers a comprehensive technical breakdown of the AlphaFold 3 architecture, focusing on its design and functionality for predicting protein structures. It serves as a resource for understanding the underlying mechanisms of this molecular tool.

PoseBench is a comprehensive benchmarking tool designed for evaluating protein-ligand structure prediction methods. It facilitates the comparison of various inference methods and provides datasets for benchmarking, making it a valuable resource in computational chemistry and molecular biology.

PiFold is a tool designed for effective and efficient protein inverse folding, generating protein sequences that fold into specified structures. It employs novel features and a graph neural network approach to enhance the accuracy and speed of protein design.

TankBind is an open-source tool designed for predicting the binding structures and affinities of drugs to proteins using a trigonometry-aware neural network. It supports high-throughput virtual screening and provides scripts for dataset construction and evaluation.

ImmuneBuilder is a deep learning tool designed to predict the structures of immune receptor proteins, including antibodies, nanobodies, and T-cell receptors. It offers state-of-the-art accuracy and speed in structure prediction, making it a valuable resource for researchers in molecular biology and biotherapeutics.

Invariant Point Attention is a standalone PyTorch module designed for coordinate refinement in protein structures, particularly utilized within the AlphaFold2 framework. It allows for the processing of molecular representations to enhance the accuracy of protein folding predictions.

AF2Complex is a tool designed to predict and model protein complexes using AlphaFold deep learning models. It enhances the capabilities of AlphaFold to accurately predict protein-protein interactions and supports various features for complex modeling and evaluation.

DeepAb is a tool for predicting the structure of antibodies from their sequences using deep learning models. It provides scripts for generating predictions, annotating structures, and scoring designed sequences, making it relevant for molecular biology and computational chemistry.

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.

Uni-Fold-jax is a trainable implementation of a deep protein folding model based on AlphaFold, allowing users to train their own models for predicting protein structures. It provides tools for preparing data, training models, and inferring protein structures from input sequences.

IntelliFold is a controllable foundation model that predicts the structures of biomolecules, particularly proteins. It provides a framework for evaluating its performance against other leading methods and offers a server for convenient predictions.

OntoProtein is a knowledge-enhanced protein language model that integrates Gene Ontology for improved protein function and structure prediction. It provides a large-scale dataset, ProteinKG25, for pretraining and fine-tuning on various protein-related tasks.

SMACT is a Python package designed for materials design and informatics, focusing on rapid screening of hypothetical materials and predicting their properties. It utilizes data about chemical elements to facilitate the generation and optimization of compositions, making it a valuable resource in computational chemistry.

FlowDock is a geometric flow matching model designed for generative protein-ligand docking and affinity prediction. It provides tools for predicting molecular interactions and includes datasets for training and evaluation, making it a valuable resource in computational chemistry and molecular biology.