/tools

This repository contains input files and datasets for the Folding@home efforts to understand and target the SARS-CoV-2 virus with small molecule and antibody therapeutics. It supports molecular dynamics simulations and provides resources for researchers working on COVID-19 related molecular studies.

RFantibody is a pipeline for the structure-based design of de novo antibodies and nanobodies. It utilizes methods for protein backbone design, sequence design, and in silico filtering of designs, making it a comprehensive tool for antibody engineering.

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.

ANARCI is a tool for antibody numbering and antigen receptor classification, utilizing alignment to germline sequences for accurate numbering. It supports various numbering schemes and outputs detailed alignment statistics, making it valuable for researchers working with antibodies.

This repository provides scripts for evolving human antibodies based on general protein language models. It allows users to recommend mutations to antibody sequences and reproduce analyses from a related research paper.

IgLM is a tool for generative language modeling aimed at antibody design. It allows users to generate unique antibody sequences and evaluate their likelihood based on specified parameters, facilitating the design and optimization of antibodies.

IgGM is a generative foundation model aimed at antibody design, enabling the creation of novel antibodies and nanobodies through various design tasks such as affinity maturation and structure prediction. It provides tools for designing sequences based on given frameworks and predicting their structures.

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.

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.

AbLang is a language model tailored for antibodies that generates representations for antibody sequences and restores missing residues. It leverages a large dataset of antibody sequences to improve predictions and design applications in molecular biology.

AntiFold predicts sequences that fit into antibody variable domain structures, allowing for the design and sampling of antibody sequences with high structural agreement to experimental data. It utilizes the ESM-IF1 model and is fine-tuned on existing antibody structures, making it a valuable tool for antibody design.

FLAb is a dataset designed for training and benchmarking AI models in therapeutic antibody design, offering extensive data on properties such as binding affinity and thermostability. It serves as a centralized resource for researchers in protein design, facilitating the development of optimized antibody candidates.

AbMAP is a Protein Language Model customized for antibodies, designed to predict structure and functional properties while analyzing B-cell repertoires. It utilizes in-silico mutagenesis and provides embeddings for antibody sequences, making it a useful tool in antibody research and design.

dyMEAN is a tool for end-to-end full-atom antibody design, enabling the generation and optimization of antibody structures based on specific epitope definitions. It includes functionalities for complex structure prediction and affinity optimization, making it a valuable resource in drug discovery and protein design.

PPIFlow is a framework for the de novo generation of high-affinity biological binders, including antibodies and nanobodies. It integrates a design workflow that supports various tasks such as binder design, antibody design, and motif scaffolding, utilizing deep learning techniques for protein structure generation.

MEAN is a tool for conditional antibody design utilizing a multi-channel equivariant attention network. It provides functionalities for redesigning antibody CDRs and optimizing binding affinities, making it a valuable resource in the field of molecular design and drug discovery.

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.

UniMoMo is a generative modeling tool designed for de novo binder design, enabling the creation of small molecules, peptides, and antibodies. It provides functionalities for training and evaluating models to generate molecular candidates based on specified configurations.

HuDiff is a tool designed for the humanization of antibodies and nanobodies using diffusion models. It includes training and evaluation scripts, as well as preprocessed datasets for developing humanized antibodies from mouse antibodies.

GearBind is a pretrainable geometric graph neural network that predicts changes in protein-protein binding affinity. It is pretrained on CATH and fine-tuned on SKEMPI, providing tools for inference and dataset processing related to antibody affinity maturation.

The 'walk-jump' repository provides an implementation of discrete Walk-Jump Sampling (dWJS) for training and sampling in protein design. It includes functionalities for evaluating large molecule descriptors and assessing sample quality, making it a valuable tool in molecular design and optimization.

The 'walk-jump' repository provides an open-source implementation of discrete Walk-Jump Sampling (dWJS) for protein design. It includes functionalities for training models and sampling, aimed at discovering and optimizing protein sequences.

Origin-1 is a generative AI platform designed for the de novo design of antibodies targeting novel epitopes. It includes data on binding affinity and optimization results, making it a valuable resource for antibody development in molecular biology.

DeepNano is a tool designed for predicting interactions between nanobodies and antigens using ensemble deep learning and protein language models. It provides code and model weights for various prediction tasks related to protein-protein interactions.