Literature DB >> 31395756

A robotic platform for flow synthesis of organic compounds informed by AI planning.

Connor W Coley1, Dale A Thomas1,2, Justin A M Lummiss3, Jonathan N Jaworski3, Christopher P Breen3, Victor Schultz1, Travis Hart1, Joshua S Fishman2, Luke Rogers1, Hanyu Gao1, Robert W Hicklin3, Pieter P Plehiers1, Joshua Byington1, John S Piotti2, William H Green1, A John Hart2, Timothy F Jamison4, Klavs F Jensen5.   

Abstract

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Year:  2019        PMID: 31395756     DOI: 10.1126/science.aax1566

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  63 in total

1.  Radial flow system decouples reactions in automated synthesis of organic molecules.

Authors:  Klavs F Jensen
Journal:  Nature       Date:  2020-03       Impact factor: 49.962

2.  In-Line Purification: A Key Component to Facilitate Drug Synthesis and Process Development in Medicinal Chemistry.

Authors:  Nopphon Weeranoppanant; Andrea Adamo
Journal:  ACS Med Chem Lett       Date:  2019-12-12       Impact factor: 4.345

3.  Computational planning of the synthesis of complex natural products.

Authors:  Barbara Mikulak-Klucznik; Patrycja Gołębiowska; Alison A Bayly; Oskar Popik; Tomasz Klucznik; Sara Szymkuć; Ewa P Gajewska; Piotr Dittwald; Olga Staszewska-Krajewska; Wiktor Beker; Tomasz Badowski; Karl A Scheidt; Karol Molga; Jacek Mlynarski; Milan Mrksich; Bartosz A Grzybowski
Journal:  Nature       Date:  2020-10-13       Impact factor: 49.962

4.  Inferring experimental procedures from text-based representations of chemical reactions.

Authors:  Alain C Vaucher; Philippe Schwaller; Joppe Geluykens; Vishnu H Nair; Anna Iuliano; Teodoro Laino
Journal:  Nat Commun       Date:  2021-05-06       Impact factor: 14.919

Review 5.  Automation and data-driven design of polymer therapeutics.

Authors:  Rahul Upadhya; Shashank Kosuri; Matthew Tamasi; Travis A Meyer; Supriya Atta; Michael A Webb; Adam J Gormley
Journal:  Adv Drug Deliv Rev       Date:  2020-11-24       Impact factor: 15.470

6.  Combining generative artificial intelligence and on-chip synthesis for de novo drug design.

Authors:  Francesca Grisoni; Berend J H Huisman; Alexander L Button; Michael Moret; Kenneth Atz; Daniel Merk; Gisbert Schneider
Journal:  Sci Adv       Date:  2021-06-11       Impact factor: 14.136

7.  Evaluating and clustering retrosynthesis pathways with learned strategy.

Authors:  Yiming Mo; Yanfei Guan; Pritha Verma; Jiang Guo; Mike E Fortunato; Zhaohong Lu; Connor W Coley; Klavs F Jensen
Journal:  Chem Sci       Date:  2020-11-23       Impact factor: 9.825

8.  Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning.

Authors:  Xiaoxue Wang; Yujie Qian; Hanyu Gao; Connor W Coley; Yiming Mo; Regina Barzilay; Klavs F Jensen
Journal:  Chem Sci       Date:  2020-09-14       Impact factor: 9.825

Review 9.  Digitising chemical synthesis in automated and robotic flow.

Authors:  Tomas Hardwick; Nisar Ahmed
Journal:  Chem Sci       Date:  2020-10-07       Impact factor: 9.825

10.  A community-powered search of machine learning strategy space to find NMR property prediction models.

Authors:  Lars A Bratholm; Will Gerrard; Brandon Anderson; Shaojie Bai; Sunghwan Choi; Lam Dang; Pavel Hanchar; Addison Howard; Sanghoon Kim; Zico Kolter; Risi Kondor; Mordechai Kornbluth; Youhan Lee; Youngsoo Lee; Jonathan P Mailoa; Thanh Tu Nguyen; Milos Popovic; Goran Rakocevic; Walter Reade; Wonho Song; Luka Stojanovic; Erik H Thiede; Nebojsa Tijanic; Andres Torrubia; Devin Willmott; Craig P Butts; David R Glowacki
Journal:  PLoS One       Date:  2021-07-20       Impact factor: 3.240

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