Virtual discovery of melatonin receptor ligands to modulate circadian rhythms

Stein, Reed M.; Kang, Hye Jin; McCorvy, John D.; Glatfelter, Grant C.; Jones, Anthony; Che, Tao; Slocum, Samuel T.; Huang, Xi Ping; Savych, Olena; Moroz, Yurii S.; Stauch, Benjamin; Johansson, Linda C.; Cherezov, Vadim; Kenakin, Terry; Irwin, John J.; Shoichet, Brian K.; Roth, Bryan L.; Dubocovich, Margarita L.

doi:10.1038/s41586-020-2027-0

Cited by 209 publications

(225 citation statements)

References 60 publications

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“…Interestingly, DIV880 is one of the few high‐affinity compounds belonging to a different chemotype among approximately 2000 “classical” melatonergic ligands that were designed based on melatonin structure (see Table 1 for chemical family details). Only very recently, new, mostly MT 2 ‐ but also MT 1 ‐selective agonists of novel chemotypes have been discovered by virtual screening of the ZINC database using the MT 1 and MT 2 crystal structures 135,136 (see discussion in Section 9).…”

Section: The Alkoxy and N‐acylamino Decorationsmentioning

confidence: 99%

“…The newly described high‐resolution MT 1 and MT 2 crystal structures 125,126 are expected to help in the discovery of ligands with new chemotypes using methods of structure‐based drug design 155 . Indeed, virtual screening of the ZINC database has led to the recent discovery of potent and selective melatonin receptor agonists and partial agonists 135,136 …”

Section: Current and Future Perspectivementioning

confidence: 99%

“…Moreover, while in the classical ligand‐based approach the H‐bond acceptors involved in essential interactions with the side chains of Asn and Gln residues were almost exclusively an alkoxy group and a carbonyl amide function, the structure‐based docking screen is able to detect a much wider range of possible H‐bond acceptor groups making the discovery of novel chemotype ligands possible. For example, in some ligands recently discovered by a large‐scale virtual screening using the 3D crystal structure of the agonist‐bound MT 1 receptor, 135 esters ( 52 ), pyridines ( 53 ), and benzodioxoles ( 54 ) were present as H‐bond acceptors interacting with Asn162 (see Figure 8 for structures).…”

Section: Current and Future Perspectivementioning

confidence: 99%

“…Selection of novel chemotype ligands. These compounds were discovered by virtual screening 135 of large compound libraries to the recent agonist‐bound MT 1 and MT 2 crystal structures 125,126 ( 52 and 54 : non‐selective agonists, 53 : MT 1 ‐selective agonist, 55 : MT 2 ‐selective agonist, 56 and 57 : MT 1 ‐selective inverse agonists, 58 : MT 2 ‐selective partial agonist with β‐arrestin‐biased MT 1 ‐signaling…”

Section: Current and Future Perspectivementioning

confidence: 99%

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Melatonin receptor ligands: A pharmaco‐chemical perspective

Boutin

Witt‐Enderby

Sotriffer

et al. 2020

Journal of Pineal Research

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Melatonin MT1 and MT2 receptor ligands have been vigorously explored for the last 4 decades. Inspection of approximately 80 publications in the field revealed that most melatonergic ligands were structural analogues of melatonin combining three essential features of the parent compound: an aromatic ring bearing a methoxy group and an amide side chain in a relative arrangement similar to that present in melatonin. While several series of MT2‐selective agents—agonists, antagonists, or partial agonists—were reported, the field was lacking MT1‐selective agents. Herein, we describe various approaches toward the development of melatonergic ligands, keeping in mind that most of the molecules/pharmacophores obtained were essentially melatonin copies, even though diverse tri‐ or tetra‐cyclic compounds were explored. In addition to lack of structural diversity, only few studies examined the activity of the reported melatonergic ligands in vivo. Moreover, an extensive pharmacological characterization including biopharmaceutical stability, pharmacokinetic properties, specificity toward other major receptors to name a few remained scarce. For example, many of the antagonists described were not stable in vivo, were not selective for the melatonin receptor subtype of interest, and were not fully characterized from a pharmacological standpoint. Indeed, virtual screening of large compound libraries has led to the recent discovery of potent and selective melatonin receptor agonists and partial agonists of new chemotypes. Having said this, the melatonergic field is still lacking subtype‐selective melatonin receptor antagonists “active” in vivo, which are critical to our understanding of melatonin and melatonin receptors’ role in basic physiology and disease.

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Section: The Alkoxy and N‐acylamino Decorationsmentioning

confidence: 99%

Section: Current and Future Perspectivementioning

confidence: 99%

Section: Current and Future Perspectivementioning

confidence: 99%

Section: Current and Future Perspectivementioning

confidence: 99%

See 2 more Smart Citations

Melatonin receptor ligands: A pharmaco‐chemical perspective

Boutin

Witt‐Enderby

Sotriffer

et al. 2020

Journal of Pineal Research

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“…Recently, there is a growing trend towards ultra-high throughput pipelines for computational drug discovery. Examples include the identification of novel β-lactamase inhibitors and dopamine receptor agonists [23], melatonin receptor ligands [24], or protein-protein interaction inhibitors [25]. Even though these results are exiting and promising, they come with a major restriction.…”

Section: Computational Drug Discoverymentioning

confidence: 99%

Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors with in silico Driven Library Prioritization and MicroScale Thermophoresis

Kaiser¹,

Plach²,

Schubert³

et al. 2020

Preprint

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Accelerated development of lead structures is of high interest to the pharmaceutical industry in order to decrease development times and costs. We showcase how an intelligent combination of AI-based drug screening with stateof-the-art biophysics drives the rapid identification of novel inhibitor structures with high chemical diversity for cGMP-dependent 3',5'-cyclic phosphodiesterase (PDE2). The starting point was an off-the-shelve chemical library of two million drug-like compounds. In a single in silico reduction step, we short-listed 125 compounds -the focused library -as potential binders to PDE2 and tested their binding behavior in vitro using MicroScale Thermophoresis (MST). Of this focused library, seven compounds indicated binding to PDE2, translating to a hit rate of 6%. Three of these compounds have affinities in the lower micromolar range. The compound with the highest affinity showed a K D of 10 µM and is thus an excellent starting point for further medicinal chemistry optimization. The results show how innovative and structure-driven in silico approaches and biophysics can be used to accelerate drug discovery and to obtain new molecular scaffolds at a fraction of the costs and time -compared with standard high-throughput screening.

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ClassyPose: A Machine‐Learning Classification Model for Ligand Pose Selection Applied to Virtual Screening in Drug Discovery

Tran‐Nguyen,

Camproux,

Taboureau

2024

Advanced Intelligent Systems

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Determining the target‐bound conformation of a drug‐like molecule is a crucial step in drug design, as it affects the outcome of virtual screening (VS), and paves the way for hit‐to‐lead and lead optimization. While most docking programs usually manage to produce at least a near‐native pose for a bioactive molecule inside its binding pocket, their integrated classical scoring functions (SFs) generally fail to prioritize this pose. Many studies have been carried out to tackle this SF problem, offering multiple pose refinement and/or classification methods, albeit with limitations. This study presents a new support vector machine model for pose classification, called “ClassyPose”, which predicts the probability that a receptor‐bound ligand conformation could be near‐native, without any additional pose optimization step. Trained on protein‐ligand extended connectivity features extracted from over 21 600 crystal and docking poses of diverse ligands, this model outperformed other machine‐learning algorithms and three existing SFs in terms of docking power, identifying the native ligand pose as top‐ranked solution for more than 90% of entries in two test sets. It also achieved high specificity (above 0.96), and improved VS performance when used for pose selection. This efficient, user‐friendly tool and all related data are available at https://github.com/vktrannguyen/Classy_Pose.

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Virtual discovery of melatonin receptor ligands to modulate circadian rhythms

Cited by 209 publications

References 60 publications

Melatonin receptor ligands: A pharmaco‐chemical perspective

Melatonin receptor ligands: A pharmaco‐chemical perspective

Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors with in silico Driven Library Prioritization and MicroScale Thermophoresis

ClassyPose: A Machine‐Learning Classification Model for Ligand Pose Selection Applied to Virtual Screening in Drug Discovery

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