/tools

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.

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.

ChatDrug is a tool for conversational drug editing that utilizes retrieval and domain feedback to assist in the design and modification of small molecules, peptides, and proteins. It includes datasets for training and evaluation, making it a comprehensive resource for drug discovery.

libmolgrid is a comprehensive library designed for fast, GPU-accelerated molecular gridding, facilitating deep learning workflows in molecular modeling. It supports the preparation of molecular data for training machine learning models, making it useful for applications in drug discovery and molecular simulations.

NoLabs is an open-source biolab that facilitates experiments in bioinformatics and drug discovery. It includes features for protein design, molecular simulations, and a workflow engine to manage various bioinformatics tasks.

DeepAffinity is a deep learning model that predicts the affinity between proteins and compounds by integrating recurrent and convolutional neural networks. It utilizes both labeled and unlabeled data to enhance prediction accuracy and interpretability in drug discovery.

TransformerCPI is a tool designed to improve the prediction of compound-protein interactions through a sequence-based deep learning approach utilizing self-attention mechanisms. It includes datasets and trained models to facilitate drug-target identification and virtual screening.

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.

p4vasp is a visualization tool for VASP (Vienna Ab initio Simulation Package) outputs, facilitating the analysis of quantum mechanical and molecular dynamics simulations. It provides a graphical interface and command-line utilities for users to interact with simulation data effectively.

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.

ChemPlot is a Python library that enables users to visualize the chemical space of molecular datasets in two dimensions. It employs various dimensionality reduction techniques and similarity algorithms to group similar molecules, facilitating the analysis of molecular properties.

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.

The CHEAP repository provides a framework for the joint embedding of protein sequences and structures using compressed representations. It allows users to obtain embeddings that can be utilized in various applications related to protein design and molecular simulations.

The Folding@home client facilitates distributed computing to simulate protein folding processes, which are crucial for understanding various diseases. It allows users to contribute to one of the largest supercomputers dedicated to molecular simulations.

SaprotHub is a platform that democratizes protein language model training and sharing, enabling biologists to fine-tune and utilize protein models without requiring advanced machine learning expertise. It supports various protein prediction tasks, facilitating collaboration and resource sharing within the protein research community.

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.

PINDER is a comprehensive dataset and evaluation resource for protein-protein interactions, aimed at enhancing the training and evaluation of docking algorithms. It includes a large collection of protein structures and associated metadata, making it a valuable resource for researchers in molecular biology and computational chemistry.

DataWarrior is a software tool for interactive data analysis and visualization, particularly useful for cheminformaticians due to its native support for chemical structures and reactions. It provides rich functionality for analyzing molecular data and visualizing chemical information.

BAMBOO is an AI-driven machine learning force field designed for precise and efficient simulations of lithium battery electrolytes. It provides tools for training models and running molecular dynamics simulations, along with datasets for training and validation.

PMDM is a software tool that enables the generation of 3D bioactive molecules and lead optimization by utilizing a dual diffusion model. It supports molecular docking and provides benchmarks for evaluating generated molecules, making it a valuable resource for drug discovery and molecular design.

ProSST is an advanced hybrid language model designed for directed protein evolution, enabling zero-shot prediction of mutant effects. It utilizes a pre-trained transformer model to analyze protein sequences and structures, making it a valuable tool for protein design and benchmarking in computational biology.

BioSimSpace is an interoperable Python framework that facilitates biomolecular simulations. It allows users to create and manage workflows for molecular simulations, making it suitable for various applications in computational chemistry and drug discovery.

MolMapNet is a deep learning framework that utilizes knowledge-based molecular representations to predict molecular properties. It provides tools for feature extraction, distance calculation, and visualization, making it suitable for tasks in drug discovery and molecular property prediction.