NURA: A curated dataset of nuclear receptor modulators

Valsecchi, Cecile; Grisoni, Francesca; Motta, Stefano; Bonati, Laura; Ballabio, Davide

doi:10.1016/j.taap.2020.115244

Cited by 22 publications

(19 citation statements)

References 42 publications

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“…We created a publicly available dataset (NURA-NUclear Receptor Activity dataset) 5 containing annotations for binding, agonism and antagonism activity for 15,206 molecules and 8 selected NRs taken from 4 public databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Each bioactivity type for a given receptor was considered as a task (e.g., binding activity for androgen receptor), obtaining a total of 30 tasks.…”

Section: Methodsmentioning

confidence: 99%

Nuclear receptor modulators: Catching information by machine learning

et al. 2021

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Nuclear receptors (NRs) are involved in fundamental human health processes and are a relevant target for toxicological risk assessment. To help prioritize chemicals that can mimic natural hormones and be endocrine disruptors, computational models can be a useful tool.1,2 In this work we i) created an exhaustive collection of NR modulators and ii) applied machine learning methods to fill the data-gap and prioritize NRs modulators by building predictive models.

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Section: Methodsmentioning

confidence: 99%

Nuclear receptor modulators: Catching information by machine learning

et al. 2021

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“…4A). Furthermore, the scaffolds of compounds 1 and 13 were not present in any of the 15,247 molecules annotated in the Nuclear Receptor Activity (NURA) dataset (52). Compound 15, the most potent LXR agonist, had the lowest fragment similarity to the two closest fine-tuning molecules (table S6).…”

Section: Bioactivity Determinationmentioning

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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“…Compound 1 (two-fold LXRα activation) was included in the follow-up study because of its novel atomic scaffold, 37 which is not present in any molecule annotated for LXRs in ChEMBL27 or in a repository for nuclear receptor bioactivity. 45…”

Section: Microfluidics-assisted Synthesis 'On-chip' and First-pass Screeningmentioning

confidence: 99%

“…Furthermore, the scaffolds of compounds 1 and 13 were not present in any of the 15,247 molecules annotated in the Nuclear Receptor Activity (NURA) dataset. 45 Compound 15, the most potent LXRβ agonist, had the lowest fragment similarity to the fine-tuning molecules, as captured by Tanimoto similarity on Morgan molecular fingerprints 46 (Supporting Table S6). These results corroborate the capacity of the computational pipeline to explore narrow regions of the chemical space defined by the known LXR agonists, while at the same time providing hitherto unexplored molecular cores for further compound optimization.…”

Section: Bioactivity Determinationmentioning

confidence: 99%

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

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Huisman²,

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et al. 2020

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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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NURA: A curated dataset of nuclear receptor modulators

Cited by 22 publications

References 42 publications

Nuclear receptor modulators: Catching information by machine learning

Nuclear receptor modulators: Catching information by machine learning

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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