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      Crowdsourcing and open innovation in drug discovery: recent contributions and future directions

      review-article
      Author(s): 1 , * , 2
      Publication date (Electronic):
      Journal: Drug Discovery Today
      Publisher: Elsevier Ltd.

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          Highlights

          • •

            Recent use and future directions of Crowdsourcing and Open Innovation in drug discovery are presented.

          • •

            Pharmaceutical companies’ online presentation of their own engagement and use of Open Innovation is assessed and compared.

          Abstract

          The past decade has seen significant growth in the use of ‘crowdsourcing’ and open innovation approaches to engage ‘citizen scientists’ to perform novel scientific research. Here, we quantify and summarize the current state of adoption of open innovation by major pharmaceutical companies. We also highlight recent crowdsourcing and open innovation research contributions to the field of drug discovery, and interesting future directions.

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          Most cited references49

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          Planning chemical syntheses with deep neural networks and symbolic AI

          Mike Preuss, Mark Waller, Marwin H. S. Segler (2018)
          To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.
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            Marginality and Problem-Solving Effectiveness in Broadcast Search

            Lars Jeppesen, Karim R. Lakhani (2010)
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              A robotic platform for flow synthesis of organic compounds informed by AI planning

              Connor Coley, Dale A Thomas, Justin Lummiss … (2019)
              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.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Drug Discov Today
                Title: Drug Discovery Today
                Publisher: Elsevier Ltd.
                ISSN (Print): 1359-6446
                ISSN (Electronic): 1878-5832
                Publication date PMC-release: 2 October 2020
                Publication date (Electronic): 2 October 2020
                Affiliations
                [1 ]Chemical Computing Group, Montreal, QC H3A 2R7, Canada
                [2 ]Alkermes, Inc. 852 Winter Street, Waltham, MA 02451-1420, USA
                Author notes
                [* ]Corresponding author:
                Article
                Publisher Item ID: S1359-6446(20)30377-9
                DOI: 10.1016/j.drudis.2020.09.020
                PMC ID: 7529695
                PubMed ID: 33011343
                SO-VID: 9e5fd193-fe6a-483e-a338-85dda8c87069
                Copyright © © 2020 Elsevier Ltd. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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                Subject: Review
                Subject: Post Screen

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
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                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

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