Algorithm-Driven, Phenotype-Directed Bioactive Molecular Discovery

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The paper studies a fully closed-loop, algorithm-driven approach for phenotype-directed small-molecule discovery, aiming to replace protein-target–centric iterative cycles with an assay-guided design–make–test workflow. Using a virtual reaction space built from substrate/co-substrate pairs, the authors algorithmically design reaction batches, automatically execute them, and screen products in a cell painting phenotypic assay, with subsequent rounds directed by observed hits until a user-defined endpoint is reached. As an example, Rh-catalysed annulations of hydoxamate esters with alkene/alkyne co-substrates, coupled to the cell painting assay, enabled discovery and structural evolution of a series of tubulin modulators. The workflow is presented as agnostic to both chemistry and assay modality, but the explicit limitation is that the work is a preprint under review rather than peer-reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract The discovery of bioactive small molecules is dominated by iterative design-make-purify-test cycles focused on specific protein targets. In contrast, phenotype-driven discovery can yield bioactive molecules with unexpected mechanisms of action, and open paths to first-in-class drugs. Here, we present a fully closed-loop, algorithm-driven workflow for phenotypic-driven molecular discovery. Initially, a large virtual reaction space is constructed from pairs of potential substrates and co-substrates. Batches of reactions are then algorithmically-designed and automatically executed, and the products screened in a phenotypic assay; based on observed hits, the algorithm then directs subsequent round(s) of discovery and optimisation until a user-defined end-point is reached. The approach was exemplified using Rh-catalysed annulations of hydoxamate esters with alkene/alkyne co-substrates, coupled with the cell painting assay, and enabled the discovery and structural evolution of a series of tubulin modulators. Because the workflow is agnostic to both the chemistry and the assay modality, the approach would be generalisable for the automated function-directed exploration of chemical space. The approach has the potential to accelerate the discovery of novel chemical probes and to unlock new opportunities for drug discovery.
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Algorithm-Driven, Phenotype-Directed Bioactive Molecular Discovery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Algorithm-Driven, Phenotype-Directed Bioactive Molecular Discovery Adam Nelson, Amalia-Sofia Piticari, Samuel Griggs, Laura Crawford, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8058819/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The discovery of bioactive small molecules is dominated by iterative design-make-purify-test cycles focused on specific protein targets. In contrast, phenotype-driven discovery can yield bioactive molecules with unexpected mechanisms of action, and open paths to first-in-class drugs. Here, we present a fully closed-loop, algorithm-driven workflow for phenotypic-driven molecular discovery. Initially, a large virtual reaction space is constructed from pairs of potential substrates and co-substrates. Batches of reactions are then algorithmically-designed and automatically executed, and the products screened in a phenotypic assay; based on observed hits, the algorithm then directs subsequent round(s) of discovery and optimisation until a user-defined end-point is reached. The approach was exemplified using Rh-catalysed annulations of hydoxamate esters with alkene/alkyne co-substrates, coupled with the cell painting assay, and enabled the discovery and structural evolution of a series of tubulin modulators. Because the workflow is agnostic to both the chemistry and the assay modality, the approach would be generalisable for the automated function-directed exploration of chemical space. The approach has the potential to accelerate the discovery of novel chemical probes and to unlock new opportunities for drug discovery. Physical sciences/Chemistry/Chemical synthesis/Chemical libraries/Combinatorial libraries Biological sciences/Chemical biology/Chemical tools Physical sciences/Mathematics and computing/Scientific data Physical sciences/Chemistry/Medicinal chemistry/Drug discovery and development Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupportingInformation05.11.25.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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