/tools

The DeepLearningExamples repository by NVIDIA provides state-of-the-art deep learning scripts that can be utilized for various applications, including drug discovery. It offers easy-to-train and deploy models that leverage NVIDIA's deep learning software stack.

Claude Scientific Skills is a collection of 140 ready-to-use scientific skills that enable users to perform complex workflows in drug discovery and cheminformatics. It includes functionalities for molecular property prediction, virtual screening, and molecular docking, making it a valuable resource for researchers in computational chemistry and molecular biology.

DeepChem provides an open-source toolchain that facilitates the application of deep learning in drug discovery, quantum chemistry, and biology. It supports various molecular tasks such as property prediction, molecular generation, and offers extensive tutorials for users to learn and apply these techniques.

Boltz is a family of models designed for predicting biomolecular interactions, including binding affinities. It aims to provide accurate in silico screening for drug discovery, leveraging deep learning techniques to enhance molecular design processes.

The 'Awesome AI for Science' repository is a curated collection of resources related to AI applications in various scientific fields, particularly focusing on drug discovery and molecular design. It includes models, datasets, and frameworks that facilitate the prediction and generation of molecular properties and structures.

Chemprop is a machine learning package that utilizes message passing neural networks to predict various molecular properties. It is particularly useful in drug discovery, enabling researchers to assess properties such as ADMET and binding affinity.

TorchDrug is a PyTorch-based machine learning toolbox tailored for drug discovery, enabling users to predict molecular properties and work with graph-structured data. It provides a range of datasets and models for various tasks in molecular machine learning.

The Therapeutics Data Commons (TDC) is an open-source initiative that facilitates the development and evaluation of AI methods for drug discovery. It offers ready-to-use datasets, benchmarks for model comparison, and tools for predicting molecular properties and generating new biomedical entities.

Graphein is a protein and interactomic graph library that enables the creation of geometric representations of protein and RNA structures, as well as biological interaction networks. It supports various molecular types and provides functionalities for graph construction, visualization, and analysis, making it a valuable resource for molecular design and drug discovery.

DeepPurpose is a deep learning library that facilitates the prediction of drug-target interactions, drug properties, and protein functions. It supports various molecular encoding tasks and provides tools for drug repurposing and virtual screening.

PaddleHelix is a bio-computing platform that leverages deep learning for drug discovery, vaccine design, and precision medicine. It offers various applications including molecular property prediction, drug-target interaction prediction, and molecular generation, along with advanced protein structure prediction capabilities.

OpenBioMed is a Python deep learning toolkit designed for AI-empowered biomedicine, offering flexible APIs and over 20 tools for various applications including molecular property prediction, protein folding, and docking. It supports a wide range of molecular types and provides a unified data processing pipeline for handling multi-modal biomedical data.

TeachOpenCADD is a teaching platform designed to educate users on computer-aided drug design (CADD) through interactive Jupyter Notebooks. It covers various topics in cheminformatics and structural bioinformatics, providing practical examples and resources for students and researchers in the field.

MOSES is a benchmarking platform for molecular generation models that facilitates research in drug discovery by providing datasets and metrics to evaluate the quality and diversity of generated molecules. It implements various generative models and standardizes the evaluation process for molecular generation.

This repository provides a comprehensive list of papers and resources related to molecular and material design using generative AI and deep learning techniques. It covers various methodologies for drug design, molecular optimization, and includes datasets and benchmarks relevant to the field.

Gnina is a molecular docking program that utilizes deep learning techniques, particularly convolutional neural networks, to score and optimize ligand interactions with protein receptors. It is built on top of existing docking software and aims to enhance the accuracy and efficiency of molecular docking processes.

DGL-LifeSci is a Python package designed for applying graph neural networks in chemistry and biology. It includes functionalities for molecular property prediction, reaction prediction, and various modeling tasks relevant to drug discovery.

ChemicalX is a deep learning library designed for predicting drug-drug interactions and polypharmacy effects. It provides integrated benchmark datasets and state-of-the-art models for drug pair scoring, making it a valuable tool in the field of computational chemistry and drug discovery.

BioPandas provides tools for handling molecular structures, particularly from PDB and MOL2 files, using pandas DataFrames. It facilitates the analysis and manipulation of protein structures, making it useful for tasks in drug discovery and computational biology.

OpenChem is a deep learning toolkit that facilitates computational chemistry and drug design research. It provides utilities for data preprocessing, model training, and supports various molecular data types, enabling tasks like classification, regression, and generative modeling.

REINVENT4 is an AI molecular design tool that focuses on generating and optimizing small molecules through techniques like reinforcement learning. It supports various design tasks such as scaffold hopping and R-group replacement, making it a valuable resource for drug discovery.

The BioNeMo Framework is a suite of tools and libraries optimized for training AI models in drug discovery, enabling efficient handling of biological data. It supports various molecular modeling tasks, including property prediction and molecular design, leveraging GPU resources for enhanced performance.

TxAgent is an AI agent that provides personalized treatment recommendations by analyzing drug interactions and contraindications. It leverages a toolbox of tools to assess molecular and clinical data, ensuring that treatment strategies are tailored to individual patient characteristics.

EquiBind is a geometric deep learning model that predicts the binding location and orientation of small molecules to proteins. It utilizes SE(3)-equivariant neural networks to achieve fast and accurate predictions, making it a valuable tool in drug discovery.