/tools

GeoAB is a tool designed for realistic antibody design and reliable affinity maturation. It provides a framework for training models that can generate and optimize antibody structures, utilizing datasets for evaluation and refinement.

Rosetta-DL is a collection of deep-learning packages aimed at predicting and designing biomolecular structures, including proteins and antibodies. It includes tools for protein folding and sequence design, contributing to advancements in molecular biology and computational chemistry.

Deep H3 Loop Prediction is a tool that utilizes a deep residual network architecture to predict probability distributions of inter-residue distances and angles specifically for CDR H3 loops in antibodies. It is designed to assist in the modeling and prediction of antibody structures based on sequence input.

The Open Targets Tractability Pipeline assesses the tractability of potential drug targets based on Ensembl Gene IDs. It categorizes targets into various buckets for small molecules, antibodies, and PROTACs, providing valuable data for drug discovery efforts.

FvHallucinator is a tool that designs antibody sequences to achieve desired folding structures using a deep learning model. It employs a sequence-to-structure prediction approach to optimize the design of specific regions within antibodies, particularly the complementarity-determining regions (CDRs).

The antiberty-pytorch repository provides an unofficial re-implementation of the AntiBERTy model, which is designed to analyze and predict properties of antibody sequences using a language model approach. It includes a dataset preparation pipeline for working with observed antibody sequences, facilitating research in antibody affinity maturation.

AlphaSeq_Antibody_Dataset contains two datasets with quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide. It is designed to support protein representation learning and includes data for machine learning optimization of antibody candidates.

AbLang2 is an antibody-specific language model that addresses germline bias in antibody sequences to improve antibody design. It generates amino acid likelihoods and residue embeddings, facilitating the prediction of mutations that enhance binding properties.

The 'antibody-dl' repository is a collection of platforms, tools, and resources aimed at enhancing antibody engineering. It includes deep learning models for antibody design, structural prediction, and various databases that support antibody research and development.

SAAINT is a structural antibody parser and database that facilitates the extraction and annotation of antibody structures and their interactions with antigens from the Protein Data Bank. It provides tools for building and analyzing a comprehensive antibody database, making it useful for antibody modeling and design.

The Therapeutic Nanobody Profiler (TNP) is an open-source computational tool that characterizes and predicts the developability of nanobodies to enhance therapeutic design. It utilizes unique metrics tailored for nanobodies, based on experimental data and clinical-stage sequences, to facilitate their development as biotherapeutics.

AntiFormer is a graph-enhanced large language model designed to predict antibody binding affinity. It incorporates sequence information into a graph framework, allowing for accurate predictions that can aid in therapeutic development and diagnostics.

SelfPAD is a framework designed to improve the prediction of antibody humanness by utilizing patent data. It includes functionalities for pre-training and fine-tuning models specifically for evaluating antibody sequences.

SCALOP is a Python tool that annotates the canonical structure of antibodies based on their sequences. It supports input in various formats and utilizes dependencies like HMMER for its functionality.

ProteinTuneRL is a framework designed for optimizing protein sequences using infilling language models and reinforcement learning. It specifically supports antibody design by modifying regions of protein sequences to enhance properties like stability and binding affinity.

This tool provides a pipeline for designing and optimizing single chain variable fragment (scFv) antibodies targeting specific peptides and histone modifications. It automates the process of identifying CDR regions and optimizing antibody frameworks using AI models.

ElliDock is a tool for rigid protein-protein docking that utilizes equivariant elliptic-paraboloid interface prediction. It includes datasets for training and evaluation, as well as scripts for preprocessing and running inference on protein complexes.

The `protlib-designer` is a Python library that utilizes integer linear programming to design diverse protein libraries by optimizing mutations based on scores from deep mutational scanning data. It is particularly aimed at antibody design, allowing researchers to generate libraries that maximize diversity while minimizing specific score metrics.

The PD-1_Fab_Diffusion repository provides tools for the discovery of PD-1-targeting antibodies using AI-driven protein diffusion methods. It includes steps for sequence generation, structure prediction, and molecular docking, facilitating the design and evaluation of novel antibody candidates.

ADIOS is a tool for antibody development that utilizes game theory and machine learning to optimize antibody shapers and simulate binding interactions. It includes JAX-accelerated simulations and algorithms for viral escape, making it a relevant resource for researchers in molecular biology and computational chemistry.

The gg-dWJS repository implements a gradient-guided discrete walk-jump sampling method for generating and optimizing biological sequences, specifically antibodies. It utilizes PyTorch for training models that can generate and evaluate antibody sequences based on various criteria.

InverseFoldingEvaluation provides a framework for benchmarking various inverse folding models used in antibody CDR sequence design. It includes scripts for running models, processing data, and analyzing results, making it a valuable tool for researchers in molecular biology and protein design.

The peleke-1 repository provides fine-tuned protein language models for generating antibody sequences that target specific antigens. It streamlines the in silico design process for antibodies, making it a valuable tool in drug discovery.

AntiDIF is a tool designed for antibody-specific inverse folding using discrete diffusion methods. It allows users to perform inference on antibody structures and customize inputs for generating diverse antibody designs.