Virtual screening-driven discovery of dual 5-HT6/5-HT2A receptor ligands with pro-cognitive properties

Staroń, Jakub; Kurczab, Rafał; Warszycki, Dawid; Satała, Grzegorz; Krawczyk, Martyna; Bugno, Ryszard; Lenda, Tomasz; Popik, Piotr; Hogendorf, Adam S.; Hogendorf, Agata; Dubiel, Krzysztof; Matłoka, Mikołaj; Moszczyński-Pętkowski, Rafał; Pieczykolan, Jerzy; Wieczorek, Maciej; Zajdel, Paweł; Bojarski, Andrzej J.

doi:10.1016/j.ejmech.2019.111857

Cited by 31 publications

(18 citation statements)

References 61 publications

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“…LB techniques are generally used in the prefiltering step, as illustrated in different works that have exploited 2D fingerprints [ 81 , 82 ], 3D molecular similarity [ 83 , 84 , 85 ] and pharmacophore models [ 86 , 87 , 88 ]. To enhance the drug-likeness of the compounds, knowledge-based in silico ADMET or pan-assay interference compounds (PAINS; [ 89 ]) filters can also be applied.…”

Section: Sequential Lb and Sb Methodsmentioning

confidence: 99%

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

135

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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

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Section: Sequential Lb and Sb Methodsmentioning

confidence: 99%

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

135

View full text Add to dashboard Cite

show abstract

“…However, studies on the new groups of serotonin receptor ligands emerging in time revealed that the concept of high basicity was no longer applied to all compounds active towards 5-HTRs, and a number of ligands characterized by low basicity have recently been developed [23][24][25][26][27]. The non-amine structure of ligands is important in terms of developing new drug-like compounds, as it can help in getting rid of side effects.…”

Section: Introductionmentioning

confidence: 99%

Low Basicity as a Characteristic for Atypical Ligands of Serotonin Receptor 5-HT2

Podlewska

Bugno

Lacivita

et al. 2021

IJMS

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Serotonin receptors are extensively examined by academic and industrial researchers, due to their vital roles, which they play in the organism and constituting therefore important drug targets. Up to very recently, it was assumed that the basic nitrogen in compound structure is a necessary component to make it active within this receptor system. Such nitrogen interacts in its protonated form with the aspartic acid from the third transmembrane helix (D3x32) forming a hydrogen bond tightly fitting the ligand in the protein binding site. However, there are several recent studies that report strong serotonin receptor affinity also for compounds without a basic moiety in their structures. In the study, we carried out a comprehensive in silico analysis of the low-basicity phenomenon of the selected serotonin receptor ligands. We focused on the crystallized representatives of the proteins of 5-HT1B, 5-HT2A, 5-HT2B, and 5-HT2C receptors, and examined the problem both from the ligand- and structure-based perspectives. The study was performed for the native proteins, and for D3x32A mutants. The investigation resulted in the determination of nonstandard structural requirements for activity towards serotonin receptors, which can be used in the design of new nonbasic ligands.

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“…The range of pKi values for the presented drugs was within 5.57–9.70 (median = 7.54). The listed examples of drugs can be found in our curated database of 5-HT1A [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor

et al. 2021

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Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure–activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R2) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

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Virtual screening-driven discovery of dual 5-HT6/5-HT2A receptor ligands with pro-cognitive properties

Cited by 31 publications

References 61 publications

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Low Basicity as a Characteristic for Atypical Ligands of Serotonin Receptor 5-HT2

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor

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