An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

AI-driven GPCR analysis, engineering, and targeting

This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

A new paradigm shift in antidepressant therapy? From dual-action to multitarget-directed ligands

Major Depressive Disorder is a chronic, recurring, and potentially fatal disease affecting up to 20% of the global population. Since the monoamine hypothesis was proposed more than 60 years ago, only a few relevant advances have been achieved, with very little disease course changing, from a pharmacological perspective. Moreover, since negative efficacy studies with novel molecules are frequent, many pharmaceutical companies have put new studies on hold. Fortunately, relevant clinical studies are currently being performed, and extensive striving is being developed by universities, research centers, and other public and private institutions. Depression is no longer considered a simple disease but a multifactorial one. New research fields are emerging in what could be a paradigm shift: the multitarget approach beyond monoamines. In this review, we summarize the present and the past of antidepressant drug discovery, with the aim to shed some light on the current state of the art in clinical and preclinical advances to face this increasingly devastating disease.

pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures

MOTIVATION

G protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands.

RESULTS

Bioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation.

AVAILABILITY AND IMPLEMENTATION

pdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at http://biosig.unimelb.edu.au/pdcsm_gpcr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

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

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.

Applications of machine learning in GPCR bioactive ligand discovery

GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

Structural determinants influencing halogen bonding: a case study on azinesulfonamide analogs of aripiprazole as 5-HT1A, 5-HT7, and D2 receptor ligands

A series of azinesulfonamide derivatives of long-chain arylpiperazines with variable-length alkylene spacers between sulfonamide and 4-arylpiperazine moiety is designed, synthesized, and biologically evaluated. In vitro methods are used to determine their affinity for serotonin 5-HT_1A, 5-HT₆, 5-HT₇, and dopamine D₂ receptors. X-ray analysis, two-dimensional NMR conformational studies, and docking into the 5-HT_1A and 5-HT₇ receptor models are then conducted to investigate the conformational preferences of selected serotonin receptor ligands in different environments. The bent conformation of tetramethylene derivatives is found in a solid state, in dimethyl sulfoxide, and as a global energy minimum during conformational analysis in a simulated water environment. Furthermore, ligand geometry in top-scored complexes is also bent, with one torsion angle in the spacer (τ₂) in synclinal conformation. Molecular docking studies indicate the role of halogen bonding in complexes of the most potent ligands and target receptors.

The impact of the halogen bonding on D2 and 5-HT1A/5-HT7 receptor activity of azinesulfonamides of 4-[(2-ethyl)piperidinyl-1-yl]phenylpiperazines with antipsychotic and antidepressant properties

A series of azinesulfonamides of long-chain arylpiperazine derivatives with semi-rigid alkylene spacer was designed, synthesized, and biologically evaluated using in vitro methods for their affinity for dopaminergic D₂ and serotoninergic 5-HT_1A, 5-HT_2A, 5-HT₆ and 5-HT₇ receptors. Docking to homology models revealed a possible halogen bond formation in complexes of the most potent ligands and the target receptors. The study allowed for the identification of compound 5-({4-(2-[4-(2,3-dichlorophenyl)piperazin-1-yl]ethyl)piperidin-1-yl}sulfonyl)quinoline (21), which behaved as D₂, 5-HT_1A and 5-HT₇ receptor antagonist. In preliminary in vivo studies, compound 21 displayed distinct antipsychotic properties in the MK-801-evoked hyperactivity test in mice at a dose of 10mg/kg, and exerted antidepressant-like effect in a forced swim test in mice (MED=0.625mg/kg, i.p.).

Novel 5-HT7R antagonists, arylsulfonamide derivatives of (aryloxy)propyl piperidines: Add-on effect to the antidepressant activity of SSRI and DRI, and pro-cognitive profile

A novel series of arylsulfonamide derivatives of (aryloxy)propyl piperidines was designed to obtain potent 5-HT₇R antagonists. Among the compounds evaluated herein, 3-chloro-N-{1-[3-(1,1-biphenyl-2-yloxy)2-hydroxypropyl]piperidin-4-yl}benzenesulfonamide (25) exhibited antagonistic properties at 5-HT₇R and showed selectivity over selected serotoninergic and dopaminergic receptors, as well as over serotonin, noradrenaline and dopamine transporters. Compound 25 demonstrated significant antidepressant-like activity in the forced swim test (0.625-2.5mg/kg, i.p.) and in the tail suspension test (1.25mg/kg, i.p.), augmented the antidepressant effect of inactive doses of escitalopram (selective serotonin reuptake inhibitor) and bupropion (dopamine reuptake inhibitor) in the FST in mice, and similarly to SB-269970, exerted pro-cognitive properties in the novel object recognition task in cognitively unimpaired conditions in rats (0.3mg/kg, i.p.). Such an extended pharmacological profile, especially the augmentation effect of the identified 5-HT₇R antagonist on SSRI activity, seems promising regarding the complexity of affective disorders and potentially improved outcomes, including mnemonic performance.