/tools

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.

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.

Chemformer is a repository that implements a pre-trained transformer model for generating and predicting molecular properties, including reaction and retrosynthetic predictions. It utilizes SMILES strings for molecular representation and is aimed at enhancing molecular design and optimization tasks.

GLN is a tool for predicting retrosynthesis pathways using a Conditional Graph Logic Network. It includes datasets for training and testing models, making it useful for molecular design and generation tasks.

LocalRetro is a tool for predicting retrosynthetic pathways for organic molecules using machine learning. It implements a model that derives local reaction templates and predicts reactants based on given products, facilitating the design and generation of new molecules.

PaRoutes is a framework that benchmarks multi-step retrosynthesis methods, providing curated datasets for building retrosynthesis models. It includes scripts for analyzing route quality and diversity, making it a valuable resource for researchers in molecular design.

RetroExplainer is a deep-learning framework designed for predicting retrosynthesis pathways with a focus on molecular assembly reasoning and interpretability. It allows users to generate and analyze potential synthetic routes for chemical compounds, making it a useful tool in the field of molecular design.

GraphRetro is a tool designed for predicting retrosynthesis pathways using graph models. It employs a two-stage process to transform product molecules back to their reactants, making it useful for molecular design and synthesis planning.

This repository contains source code and documentation for a computer-assisted synthesis planning tool used to analyze reaction datasets in organic chemistry. It facilitates the extraction of templates and the training of policies for synthetic route generation.

SynPlanner is an open-source tool for computer-aided synthesis planning that enhances the flexibility of developing customized retrosynthetic solutions. It integrates advanced machine learning techniques to evaluate reaction rules and synthesizability of intermediate products, facilitating effective retrosynthetic planning.

ReTReK is a data-driven application designed for retrosynthesis planning, leveraging knowledge from US patent datasets to predict synthetic routes for target molecules. It aims to assist chemists in designing efficient synthesis pathways using various scoring metrics to evaluate potential reactions.

The 'rxn-ebm' repository implements energy-based modeling to enhance the performance of retrosynthesis models by re-ranking reactant predictions. It leverages machine learning to analyze reaction databases and improve synthesis planning for organic chemistry.

RetroBridge is a Markov bridge model that facilitates retrosynthesis planning by predicting reactants for given product molecules. It utilizes a generative framework to learn distributions in a discrete state space, achieving state-of-the-art results in retrosynthesis tasks.

Aizynthtrain provides routines and pipelines for training models that predict chemical synthesis pathways. It is designed to work with the AiZynthFinder software, facilitating the generation and optimization of synthetic routes in molecular chemistry.

The 'route-distances' repository contains tools for calculating distances between synthesis routes and clustering them, primarily aimed at developers and researchers in cheminformatics. It also incorporates a machine learning model for fast predictions of distances between synthetic routes, enhancing its utility in molecular design and retrosynthesis.

MultiStepRetrosynthesisTTL is a tool designed for predicting multistep retrosynthesis routes by utilizing a disconnection aware triple transformer loop approach. It integrates various models to enhance the prediction of synthetic pathways, making it a valuable resource for molecular design and synthesis in computational chemistry.

RetroCast is a comprehensive toolkit designed for standardizing, scoring, and analyzing multistep retrosynthesis models. It provides a unified framework for evaluating different retrosynthesis algorithms, facilitating rigorous comparisons and improving reproducibility in molecular design.

READRetro is a tool designed for natural product biosynthesis planning using retrieval-augmented dual-view retrosynthesis. It allows users to evaluate and plan retrosynthesis paths for various molecules, making it relevant for molecular design and generation tasks.

EvalRetro is a repository designed for evaluating single-step retrosynthesis algorithms. It allows users to test their own retrosynthetic predictions against benchmark datasets, facilitating the development and assessment of molecular generation models.

Syndirella is a tool designed for generating and scoring synthetically tractable elaborations of molecules derived from fragment screens. It utilizes retrosynthetic analysis and energy minimization to produce viable synthetic routes for small molecules.

The RXN reaction preprocessing repository provides tools for preprocessing datasets of chemical reactions, including standardization, filtering, and data augmentation. It facilitates the creation of flexible data pipelines for chemical reaction data, which is crucial for various molecular applications.

SynthArena is an interactive platform designed for visualizing and comparing retrosynthetic routes generated by AI models. It facilitates the evaluation of these models through standardized benchmarks and provides features for side-by-side comparison of synthetic routes.

The Twitter Retrosynthesis Bot is designed to perform retrosynthesis analysis for random molecules sourced from PubChem. It utilizes the AiZynthFinder tool to assist in generating synthetic routes, making it relevant for molecular design and cheminformatics.

The aizynthmodels repository provides tools for training and evaluating models that predict synthetic routes for molecules. It is designed to assist in the synthesis prediction process, making it a valuable resource in molecular design and cheminformatics.