Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Linkers in fragment-based drug design: an overview of the literature

INTRODUCTION

In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers.

AREAS COVERED

The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking.

EXPERT OPINION

The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.

Identification of potential matrix metalloproteinase-2 inhibitors from natural products through advanced machine learning-based cheminformatics approaches

Matrix metalloproteinase-2 (MMP-2) is capable of degrading Collage TypeIV in the vascular basement membrane and extracellular matrix. Studies have shown that MMP-2 is tightly associated with the biological behavior of malignant tumors. Therefore, the identification of inhibitors targeting MMP-2 could be effective in treating the disease by maintaining extracellular matrix homeostasis. In the pharmaceutical and biomedical fields, many computational tools are widely used, which improve the efficiency of the whole process to some extent. Apart from the conventional cheminformatics approaches (e.g., pharmacophore model and molecular docking), virtual screening strategies based on machine learning also have promising applications. In this study, we collected 2871 compound activity data against MMP-2 from the ChEMBL database and divided the training and test sets in a 3:1 ratio. Four machine learning algorithms were then selected to construct the classification models, and the best-performing model, i.e., the stacking-based fusion model with the highest AUC value in both training and test datasets, was used for the virtual screening of ZINC database. Next, we screened 17 potential MMP-2 inhibitors from the results predicted by the machine learning model via ADME/T analysis. The interactions between these compounds and the target protein were explored through molecular docking calculations, and the results showed that ZINC712249, ZINC4270723, and ZINC15858504 had lower binding free energies than the co-crystal ligand. To further examine the binding stability of the complexes, we performed molecular dynamics simulations and finally identified these three hits as the most promising natural products for MMP-2 inhibitors.

Virtual screening and molecular dynamics simulation analysis of Forsythoside A as a plant-derived inhibitor of SARS-CoV-2 3CLpro

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a more severe strain of coronavirus (CoV) that was first emerged in China in 2019. Available antiviral drugs could be repurposed and natural compounds with antiviral activity could be safer and cheaper source of medicine for SARS-CoV-2. 78 natural antiviral compounds database was identified from literature and virtual screening technique was applied to identify potential 3-chymotrypsin-like protease (3CLpro) inhibitors. Molecular docking studies were conducted to analyze the main protease (3CLpro) and inhibitors interactions with key residues of active site of target protein (PDB ID: 6LU7), active site constitute the part of active domain I and II of 3CLpro. 10 compounds with highest dock score were subjected to calculate ADMET parameters to figure out drug-likeness. Molecular dynamic (MD) simulation of the selected lead was performed by Amber simulation package to understand the conformational changes in docked complex. MD simulations analysis (RMSD, RMSF, Rg, BF, HBs, and SASA plots) of lead bounded with 3CLpro, hence revealed the important structural turns and twists during MD simulations from 0 to 100 ns. MM-PBSA/GBSA methods has also been applied for the estimation binding free energy (BFE) of the selected lead-complex. The present study has identified lead compound “Forsythoside A” an active extract of Forsythia suspense as SARS-CoV-2 3CLpro inhibitor that can block the viral replication and translation. Structural analysis of target protein and lead compound performed in this study could contribute to the development of potential drug against SARS-CoV-2 infection.

How Do Modulators Affect the Orthosteric and Allosteric Binding Pockets?

Allosteric modulators (AMs) that bind allosteric sites can exhibit greater selectivity than the orthosteric ligands and can either enhance agonist-induced receptor activity (termed positive allosteric modulator or PAM), inhibit agonist-induced activity (negative AM or NAM), or have no effect on activity (silent AM or SAM). Until now, it is not clear what the exact effects of AMs are on the orthosteric active site or the allosteric binding pocket(s). In the present work, we collected both the three-dimensional (3D) structures of receptor-orthosteric ligand and receptor-orthosteric ligand-AM complexes of a specific target protein. Using our novel algorithm toolset, molecular complex characterizing system (MCCS), we were able to quantify the key residues in both the orthosteric and allosteric binding sites along with potential changes of the binding pockets. After analyzing 21 pairs of 3D crystal or cryo-electron microscopy (cryo-EM) complexes, including 4 pairs of GPCRs, 5 pairs of ion channels, 11 pairs of enzymes, and 1 pair of transcription factors, we found that the binding of AMs had little impact on both the orthosteric and allosteric binding pockets. In return, given the accurately predicted allosteric binding pocket(s) of a drug target of medicinal interest, we can confidently conduct the virtual screening or lead optimization without concern that the huge conformational change of the pocket could lead to the low accuracy of virtual screening.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Generative chemistry: drug discovery with deep learning generative models

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

Covalent allosteric modulation: An emerging strategy for GPCRs drug discovery

Designing covalent allosteric modulators brings new opportunities to the field of drug discovery towards G-protein-coupled receptors (GPCRs). Targeting an allosteric binding pocket can allow a modulator to have protein subtype selectivity and low drug resistance. Utilizing covalent warheads further enables the modulator to increase the binding potency and extend the duration of action. This review starts with GPCR allosteric modulation to discuss the structural biology of allosteric binding pockets, the different types of allosteric modulators, as well as the advantages of employing allosteric modulation. This is followed by a discussion on covalent modulators to clarify how covalent ligands can benefit the receptor modulation and to illustrate moieties that can commonly be used as covalent warheads. Finally, case studies are presented on designing class A, B, and C GPCR covalent allosteric modulators to demonstrate successful stories on combining allosteric modulation and covalent binding. Limitations and future perspectives are also covered.

Effects of α-Mangostin Derivatives on the Alzheimer's Disease Model of Rats and Their Mechanism: A Combination of Experimental Study and Computational Systems Pharmacology Analysis

α-Mangostin (α-M) is a natural xanthone from the pericarp of fruit Garcinia mangostana and possesses versatile biological activities. α-M has a therapeutic potential to treat Alzheimer's disease (AD) because of its anti-inflammatory, antioxidative, and neuroprotective activities. However, the use of α-M for AD treatment is limited due to its cytotoxic activities and relatively low potency. Modifications of its chemical structure were needed to reduce its cytotoxicity and improve its therapeutic potential against AD. For this purpose, 16 α-M carbamate derivatives were synthesized. An animal model of AD was established, and the effects of AMG-1 on the spatial learning ability and memory ability were evaluated using behavioral tests. The effect on neuropathology was tested by histopathological evaluation, Nissl staining, and silver staining. Computational systems pharmacology analysis using the chemogenomics knowledgebase was applied for network studies. Compound-target, target-pathway, and target-disease networks were constructed, integrating both in silico analysis and reported experimental data. The results show that AMG-1 can demonstrate its therapeutic effects in a one-molecule, multiple-targets manner to remarkably ameliorate neurological changes and reverse behavioral deficits in AD model rats. The improved cognitive function and alleviated neuronal injury can be observed. The ability of AMG-1 to scavenge β-amyloid in the hippocampus was validated in AD model rats.

Profiling the Structural Determinants of Aryl Benzamide Derivatives as Negative Allosteric Modulators of mGluR5 by In Silico Study

Glutamate plays a crucial role in the treatment of depression by interacting with the metabotropic glutamate receptor subtype 5 (mGluR5), whose negative allosteric modulators (NAMs) are thus promising antidepressants. At present, to explore the structural features of 106 newly synthesized aryl benzamide series molecules as mGluR5 NAMs, a set of ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were firstly carried out applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. In addition, receptor-based analysis, namely molecular docking and molecular dynamics (MD) simulations, were performed to further elucidate the binding modes of mGluR5 NAMs. As a result, the optimal CoMSIA model obtained shows that cross-validated correlation coefficient Q² = 0.70, non-cross-validated correlation coefficient R²_ncv = 0.89, predicted correlation coefficient R²_pre = 0.87. Moreover, we found that aryl benzamide series molecules bind as mGluR5 NAMs at Site 1, which consists of amino acids Pro655, Tyr659, Ile625, Ile651, Ile944, Ser658, Ser654, Ser969, Ser965, Ala970, Ala973, Trp945, Phe948, Pro903, Asn907, Val966, Leu904, and Met962. This site is the same as that of other types of NAMs; mGluR5 NAMs are stabilized in the "linear" and "arc" configurations mainly through the H-bonds interactions, π-π stacking interaction with Trp945, and hydrophobic contacts. We hope that the models and information obtained will help understand the interaction mechanism of NAMs and design and optimize NAMs as new types of antidepressants.

Fragment-Based Approaches for Allosteric Metabotropic Glutamate Receptor (mGluR) Modulators

Metabotropic glutamate receptors (mGluR) are members of the class C G-Protein Coupled Receptors (GPCR-s) and have eight subtypes. These receptors are responsible for a variety of functions in the central and peripheral nervous systems and their modulation has therapeutic utility in neurological and psychiatric disorders. It was previously established that selective orthosteric modulation of these receptors is challenging, and this stimulated the search for allosteric modulators. Fragment-Based Drug Discovery (FBDD) is a viable approach to find ligands binding at allosteric sites owing to their limited size and interactions. However, it was also observed that the structure-activity relationship of allosteric modulators is often sharp and inconsistent. This can be attributed to the characteristics of the allosteric binding site of mGluRs that is a water channel where ligand binding is accompanied with induced fit and interference with the water network, both playing a role in receptor activation. In this review, we summarize fragment-based drug discovery programs on mGluR allosteric modulators and their contribution identifying of new mGluR ligands with better activity and selectivity.

Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors

A deep convolutional generative adversarial network (dcGAN) model was developed in this study to screen and design target-specific novel compounds for cannabinoid receptors. In the adversarial process of training, two models, the discriminator D and the generator G, are iteratively trained. D is trained to discover the hidden patterns among the input data to have the accurate discrimination of the authentic compounds and the "fake" compounds generated by G; G is trained to generate "fake" compounds to fool the well-trained D by optimizing the weights for matrix multiplication of data sampling. In order to determine the appropriate architecture and the input data structure for the involved convolutional neural networks (CNNs), the combinations of various network architectures and molecular fingerprints were explored. Well-developed CNN models including LeNet-5, AlexNet, ZFNet, and VGGNet were investigated. Four types of fingerprints, including MACCS, ECFP6, AtomPair, and AtomPair Count, were calculated to describe the small molecules with diverse structural characteristics. The limitation of generating fingerprints as output remains that the concrete molecular structures cannot be converted directly, while the generative models with convolutional networks provide promising opportunities to the screening of molecules and rational modifications afterward. This study demonstrated how computer-aided drug discovery could benefit from the recent advances in deep learning.

Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers

Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naïve Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development.

Significantly different effects of tetrahydroberberrubine enantiomers on dopamine D1/D2 receptors revealed by experimental study and integrated in silico simulation

Tetrahydroberberrubine (TU), an active tetrahydroprotoberberines (THPBs), is gaining increasing popularity as a potential candidate for treatment of anxiety and depression. One of its two enantiomers, l-TU, has been reported to be an antagonist of both D1 and D2 receptors, but the functional activity of the other enantiomer, d-TU, is still unknown. In this study, experiments were combined with in silico molecular simulations to (1) confirm and discover the functional activities of l-TU and d-TU, and (2) systematically evaluate the molecular mechanisms beyond the experimental observations. l-TU proved to be an antagonist of both D1 and D2 receptors (IC50 = 385 nM and 985 nM, respectively), while d-TU shows no affinity against either D1 or D2 receptor, based on the cAMP assay (D1 receptor) and calcium flux assay (D2 receptor). Results from both flexible-ligand docking studies and molecular dynamic (MD) simulations provided insights at the atomic level. The l-TU-bound structures predicted by MD (1) undergo an outward rotation of the extracellular helical bundles; (2) have an enlarged orthosteric binding pocket; and (3) have a central toggle switch that is prevented from rotating freely. These features are unique to the l-TU enantiomer and provide an explanation for its antagonistic behavior toward both D1 and D2 receptors. The present study provides new sight on the structural and functional relationships of l-TU and d-TU binding to dopamine receptors, and provides guidance to the rational design of novel molecules targeting these two dopamine receptors in the future.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation

With treatment benefits in both the central nervous system and the peripheral system, the medical use of cannabidiol (CBD) has gained increasing popularity. Given that the therapeutic mechanisms of CBD are still vague, the systematic identification of its potential targets, signaling pathways, and their associations with corresponding diseases is of great interest for researchers. In the present work, chemogenomics-knowledgebase systems pharmacology analysis was applied for systematic network studies to generate CBD-target, target-pathway, and target-disease networks by combining both the results from the in silico analysis and the reported experimental validations. Based on the network analysis, three human neuro-related rhodopsin-like GPCRs, i.e., 5-hydroxytryptamine receptor 1 A (5HT_1A), delta-type opioid receptor (OPRD) and G protein-coupled receptor 55 (GPR55), were selected for close evaluation. Integrated computational methodologies, including homology modeling, molecular docking, and molecular dynamics simulation, were used to evaluate the protein-CBD binding modes. A CBD-preferred pocket consisting of a hydrophobic cavity and backbone hinges was proposed and tested for CBD-class A GPCR binding. Finally, the neurophysiological effects of CBD were illustrated at the molecular level, and dopamine receptor 3 (DRD3) was further predicted to be an active target for CBD.

Allosteric Modulation of Class A GPCRs: Targets, Agents, and Emerging Concepts

G-protein-coupled receptors (GPCRs) have been tractable drug targets for decades with over one-third of currently marketed drugs targeting GPCRs. Of these, the class A GPCR superfamily is highly represented, and continued drug discovery for this family of receptors may provide novel therapeutics for a vast range of diseases. GPCR allosteric modulation is an innovative targeting approach that broadens the available small molecule toolbox and is proving to be a viable drug discovery strategy, as evidenced by recent FDA approvals and clinical trials. Numerous class A GPCR allosteric modulators have been discovered recently, and emerging trends such as the availability of GPCR crystal structures, diverse functional assays, and structure-based computational approaches are improving optimization and development. This Perspective provides an update on allosterically targeted class A GPCRs and their disease indications and the medicinal chemistry approaches toward novel allosteric modulators and highlights emerging trends and opportunities in the field.

Development and Testing of Druglike Screening Libraries

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

Progress in Allosteric Database

An allosteric mechanism refers to the biological regulation process wherein macromolecules propagate the effect of ligand binding at one site to a spatially distant orthosteric locus, thus affecting activity. The theory has remained a trending topic in biology research for over 50 years, since the understanding of allostery is fundamental for gleaning numerous biological processes and developing new drug therapies. In the past two decades, the allosteric paradigm has evolved into more descriptive models, with ever-expanding amounts of experimental data pertaining to newly identified allosteric molecules. The AlloSteric Database (ASD, accessible at http://mdl.shsmu.edu.cn/ASD ), which is a comprehensive knowledge repository, has provided the public with integrated information encompassing allosteric proteins, modulators, sites, pathways, and networks to investigate allostery since 2009. In this chapter, we introduce the history and usage of the ASD and give attention to specific applications that have benefited from the ASD.

Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations

Metabotropic glutamate receptor 5 (mGlu₅) plays a key role in synaptic information storage and memory, which is a well-known target for a variety of psychiatric and neurodegenerative disorders. In recent years, the increasing efforts have been focused on the design of allosteric modulators, and the negative allosteric modulators (NAMs) are the front-runners. Recently, the architecture of the transmembrane (TM) domain of mGlu₅ receptor has been determined by crystallographic experiment. However, it has been not well understood how the pharmacophores of NAMs accommodated into the allosteric binding site. In this study, molecular dynamics (MD) simulations were performed on mGlu₅ receptor bound with NAMs in preclinical or clinical development to shed light on this issue. In order to identify the key residues, the binding free energies as well as per-residue contributions for NAMs binding to mGlu₅ receptor were calculated. Subsequently, the in silico site-directed mutagenesis of the key residues was performed to verify the accuracy of simulation models. As a result, the shared common features of the studied 5 clinically important NAMs (mavoglurant, dipraglurant, basimglurant, STX107, and fenobam) interacting with 11 residues in allosteric site were obtained. This comprehensive study presented a better understanding of mGlu₅ receptor NAMs binding mechanism, which would be further used as a useful framework to assess and discover novel lead scaffolds for NAMs.

Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.