/tools

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.

RDKit is a comprehensive cheminformatics library that provides tools for molecular property prediction, descriptor generation, and various molecular operations. It supports both small molecules and proteins, making it a versatile tool for researchers in computational chemistry and molecular biology.

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.

RFdiffusion is an open-source method for generating protein structures, allowing for both unconditional and motif-based design. It supports various protein design challenges, including binder design and scaffold generation, making it a valuable tool in molecular biology and computational chemistry.

The ESM repository provides flagship generative models for protein sequences, allowing users to generate and predict protein structures and functions. It includes tools for both protein design and representation learning, making it a valuable resource in molecular biology.

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.

This repository provides a collection of Jupyter notebooks designed to teach practical cheminformatics using open-source software. It covers various topics including molecular property prediction, generative molecular design, and machine learning models applicable to cheminformatics workflows.

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.

BindCraft is a user-friendly pipeline for designing protein binders using advanced techniques like AlphaFold2 backpropagation and MPNN. It allows users to specify targets and generates multiple binder designs for experimental characterization.

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.

ToolUniverse is a platform that democratizes the creation of AI scientists by integrating a wide range of machine learning models, datasets, and scientific tools. It enables users to perform tasks related to molecular property prediction, molecular design, and scientific workflows, making it a versatile tool in computational chemistry and molecular biology.

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.

ChemCrow is an open-source package that augments large language models with chemical tools to solve complex chemical tasks. It integrates various databases and APIs to assist in predicting molecular properties and generating chemical reactions.

BoltzGen is a tool for designing and generating protein structures based on specified design criteria. It utilizes advanced algorithms to produce ranked sets of protein designs, facilitating the exploration of novel molecular architectures.

AiZynthFinder is a Python tool that utilizes Monte Carlo tree search and neural networks for retrosynthetic planning, allowing users to break down complex molecules into purchasable precursors. It supports customizable search algorithms and is aimed at facilitating the design and generation of molecules.

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.

AIRS is an open-source collection of software tools and datasets focused on artificial intelligence applications in quantum, atomistic, and continuum systems. It includes resources for predicting molecular properties, designing molecules, and conducting simulations, making it highly relevant to the fields of computational chemistry and molecular biology.

Foundry is a central repository for biomolecular foundation models that provides tools for training and using models for protein design, including generative models and structure prediction. It relies on AtomWorks for manipulating biomolecular structures and supports various protein design tasks.

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.

GFlowNet is a framework for generating diverse molecular candidates using flow network-based generative models. It is part of a project aimed at enhancing molecule discovery through advanced machine learning techniques.

Awesome Flow Matching is a collection of works focused on flow matching and stochastic interpolants, which are relevant for generative modeling in various domains, including molecular design. It includes applications for generating and optimizing molecular structures, particularly in the context of DNA sequence design.

The e3_diffusion_for_molecules repository provides an implementation of an E(3) equivariant diffusion model for generating 3D molecular structures. It allows for training on various molecular datasets and includes tools for analyzing the properties of generated molecules.

LigandMPNN is a package that offers inference code for generating and redesigning molecular sequences using machine learning models. It supports various configurations for designing ligands and proteins, making it a valuable tool in molecular design.

AlphaFlow is a modified version of AlphaFold that employs flow matching to generate protein conformational ensembles. It is capable of modeling both experimental and molecular dynamics ensembles, providing tools for protein design and simulation.