Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Bruns, Dominique; Merk, Daniel; Kumar, Karthiga Santhana; Baumgärtner, Martin; Schneider, Gisbert

doi:10.1002/open.201900222

Cited by 11 publications

(14 citation statements)

References 44 publications

(61 reference statements)

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“…A demonstration of the applicability of AI for drug design from scratch in lowdata situations is still to be shown. In this context, the established concept of transfer learning 155 renders "few shot" methods for generative molecular design 156 , with first pioneering examples demonstrating its practical applicability 78,79,157 . Notwithstanding, assessing the actual impact on hit and lead generation requires further validation of transfer learning methodology in different low-data situations and projects.…”

Section: Challenge 4: Reducing Cycle Timesmentioning

confidence: 99%

“…First successful syntheses of de novo generated compounds (1-4) corroborate the practical applicability of generative molecular design to drug discovery. Pioneering prospective designs, RXR agonists 1 (EC50 = 0.06±0.02 µM) and 2 (EC50 = 19.1±0.1 µM), were generated with a deep LSTM network 157 . This model was trained with SMILES strings of bioactive compounds from ChEMBL 28 and fine-tuned on nuclear hormone receptor targets using transfer learning.…”

Section: Box 3 Chemical Hypothesis Generation With Constructive Machmentioning

confidence: 99%

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Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

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472

342

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Perspective │2 Artificial intelligence (AI) tools are increasingly being applied in drug discovery. Whilst some protagonists point to vast opportunities potentially offered by such tools, others remain skeptical, waiting for a clear impact to be shown in drug discovery projects. The truth is probably somewhere between these extremes, but it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' for small-molecule drug discovery with AI and approaches to address them.

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Section: Challenge 4: Reducing Cycle Timesmentioning

confidence: 99%

Section: Box 3 Chemical Hypothesis Generation With Constructive Machmentioning

confidence: 99%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

Self Cite

472

342

View full text Add to dashboard Cite

show abstract

“…Generative deep learning models extend the capabilities of rule-based de novo molecule generators by sampling new molecules from a latent chemical space representation (20)(21)(22)(23), without the need for human-crafted molecule construction rules. Recently, the prospective applicability of "rule-free" generative deep learning for de novo molecular design has been demonstrated in combination with batch synthesis (9,10,(24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

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“…Recent prospective studies have demonstrated the practical applicability of generative deep learning for de novo molecular design. 8,9,[19][20][21] Herein, a recently published generative deep learning model 22 was adapted to generate compounds that are synthesizable on a bench-top microfluidic synthesis platform. 14,23 We challenged this automated DMTA pipeline to design liver X receptor (LXR) agonists from scratch, with minimal human interference.…”

mentioning

confidence: 99%

Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

Grisoni

Huisman²,

Button³

et al. 2020

Preprint

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Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 de novo generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass in vitro activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

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Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Cited by 11 publications

References 44 publications

Rethinking drug design in the artificial intelligence era

Rethinking drug design in the artificial intelligence era

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

Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

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