AI-assisted synthesis prediction

Q1 Pharmacology, Toxicology and Pharmaceutics

Drug Discovery Today: Technologies Pub Date : 2019-12-01 DOI:10.1016/j.ddtec.2020.06.002

Simon Johansson , Amol Thakkar , Thierry Kogej , Esben Bjerrum , Samuel Genheden , Tomas Bastys , Christos Kannas , Alexander Schliep , Hongming Chen , Ola Engkvist

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引用次数: 25

Abstract

Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper.

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人工智能辅助合成预测

近年来，人工智能技术在综合预测中的应用发展很快。本文综述了逆向合成计划、正向合成预测以及基于量子化学的反应预测模型的最新进展。除了介绍用于解决各种合成相关问题的AI/ML模型外，还涵盖了模型构建中使用的反应数据集的来源。除了预测模型之外，基于机器人的高通量实验技术将是以自动化方式进行合成的另一个关键因素。本章强调了制药工业中进行的一些最先进的高通量实验实践，以使读者了解未来化学将如何进行以更快，更便宜地制造化合物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Drug Discovery Today: Technologies Pharmacology, Toxicology and Pharmaceutics-Drug Discovery

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期刊介绍： Discovery Today: Technologies compares different technological tools and techniques used from the discovery of new drug targets through to the launch of new medicines.