Abstract

We present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 s and perform a complete search in less than 1 min. Moreover, the development of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software with high maintainability. Finally, the software is well documented to make it suitable for beginners. The software is available at http://www.github.com/MolecularAI/aizynthfinder.

Highlights

  • Synthesis planning is the process by which a chemist or a computer determines how to synthesize a specific compound

  • For the end-user we provide two interfaces: one command-line interface (CLI) and one graphical user interface (GUI) that is intended to be used in a Jupyter notebook

  • We exemplify the usages of the tool with the GUI and proceed with a comparison using the CLI

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Summary

Introduction

Synthesis planning is the process by which a chemist or a computer determines how to synthesize a specific compound This is typically carried out by retrosynthetic analysis where the desired compound is iteratively broken down into intermediates or smaller precursors until known or purchasable building blocks have been found. Such analysis was pioneered by Corey et al and was traditionally carried out by hand or by using expert systems utilizing hand-encoded rules [1,2,3]. In template-based approaches, reaction templates or rules that describe chemical transformations are manually encoded or derived from a database of known reactions, and subsequently applied to other compounds to create plausible reaction outcomes. Other template-free methods are based on graph approaches [12, 13]

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