/tools

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.

This repository contains resources and code for teaching cheminformatics, covering topics such as molecular dynamics, machine learning applications, and various cheminformatics techniques. It serves as a comprehensive educational tool for students and educators in the field of computational chemistry.

Dockstring is a Python package designed for easy molecular docking, allowing users to dock molecules from SMILES strings. It includes a highly-curated dataset and realistic benchmark tasks to aid in drug discovery efforts.

The ReinventCommunity repository provides a collection of Jupyter notebook tutorials for using REINVENT 3.2, focusing on molecular design through reinforcement learning and QSAR modeling. It includes examples of data preparation, model building, and various reinforcement learning scenarios to generate and optimize novel compounds.

Global Chem is a public dictionary that organizes common chemical names and their molecular definitions into a knowledge graph. It serves as a resource for understanding chemical compounds used in various applications, including drugs and materials.

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.

ParallelFold accelerates the AlphaFold protein structure prediction process by dividing the computational workload between CPU and GPU, allowing for faster predictions of multiple protein structures. It is particularly useful for researchers in molecular biology looking to enhance their structural predictions.

The Molecular Design Toolkit is a Python library that facilitates molecular modeling by integrating simulation, visualization, and analysis tools. It allows users to work with various molecular types and perform tasks such as geometry optimization and 3D visualization in Jupyter notebooks.

ConfGF is an implementation of Learning Gradient Fields for generating molecular conformations. It provides tools for training models on molecular datasets and generating conformations from SMILES representations, making it useful for molecular design and related applications.

3DInfomax enhances graph neural networks for predicting molecular properties by leveraging 3D molecular geometry. It provides tools for pre-training and fine-tuning models on various molecular datasets, enabling better predictions and molecular fingerprint generation.

PRODIGY is a tool designed to predict the binding affinity of protein-protein complexes based on structural data. It allows users to analyze single or multiple structures and provides detailed output on intermolecular contacts and predicted affinities.

ChemML is a machine learning and informatics program suite that facilitates the analysis and modeling of chemical and materials data. It provides tools for predicting molecular properties and supports various applications in drug discovery and materials informatics.

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.

FLEXS is an open-source environment for developing and comparing algorithms for biological sequence design. It allows users to explore fitness landscapes and implement exploration algorithms to optimize sequences based on model-guided predictions.

ChemTS is a software tool that utilizes Monte Carlo Tree Search combined with neural networks to design novel molecules with desired properties such as HOMO-LUMO gap and logP. It also allows for the design of molecules that are active against target proteins, facilitating advancements in drug discovery.

The Protein-LLM-Survey repository provides a comprehensive survey of large language models used for protein sequence modeling, understanding, and generation. It includes various methods and models that facilitate protein design and prediction of protein properties.

iCn3D is a web-based 3D structure viewer and analysis tool that enables users to visualize and analyze protein structures interactively or in batch mode. It supports various features such as VR and AR views, batch processing of structures, and detailed analysis of molecular interactions.

Graph-ViT-MLPMixer is an implementation of a model that generalizes ViT and MLP-Mixer architectures to graph data, enabling tasks such as graph classification and regression on molecular datasets. It supports various datasets, including those related to peptides and small molecules, making it relevant for molecular property prediction.

rdeditor is a simple RDKit molecule editor GUI that allows users to interactively edit molecular structures. It provides tools for adding and modifying atoms and bonds, making it useful for molecular design and visualization.

DrugOOD is a dataset curator and benchmark tool designed for AI-aided drug discovery, focusing on generating datasets for ligand and structure-based affinity prediction. It supports various noise levels and domain annotations, making it a valuable resource for researchers in molecular property prediction.

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.

DrugEx is an open-source software library designed for the de novo design of small molecules using deep learning generative models within a multi-objective reinforcement learning framework. It provides various generator architectures and scoring tools to facilitate the generation and optimization of drug-like compounds.

DeeplyTough is a tool designed for learning structural comparisons of protein binding sites using deep learning techniques. It provides built-in support for several benchmark datasets and allows users to evaluate custom datasets, making it a valuable resource for drug discovery and protein design.