Discovery of novel selective serotonin reuptake inhibitors through development of a protein-based pharmacophore

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Differentiated HT22 cells as a novel model for in vitro screening of serotonin reuptake inhibitors

The mouse hippocampal neuronal cell line HT22 is frequently used as an in vitro model to investigate the role of hippocampal cholinergic neurons in cognitive functions. HT22 cells are derived from hippocampal neuronal HT4 cells. However, whether these cells exhibit the serotonergic neuronal phenotype observed in mature hippocampal neurons has not been determined yet. In this present study, we examined whether the differentiation of HT22 cells enhances the serotonergic neuronal phenotype, and if so, whether it can be used for antidepressant screening. Our results show that differentiation of HT22 cells promoted neurite outgrowth and upregulation of N-methyl-D-aspartate receptor and choline acetyltransferase, which is similar to that observed in primary cultured hippocampal neurons. Furthermore, proteins required for serotonergic neurotransmission, such as tryptophan hydroxylase 2, serotonin (5-hydroxytryptamine, 5-HT)_1a receptor, and serotonin transporter (SERT), were significantly upregulated in differentiated HT22 cells. The transcription factor Pet-1 was upregulated during HT22 differentiation and was responsible for the regulation of the serotonergic neuronal phenotype. Differentiation also enhanced the functional serotonergic properties of HT22 cells, as evidenced by increase in intracellular 5-HT levels, serotonin transporter SERT glycosylation, and 5-HT reuptake activity. The sensitivity of 5-HT reuptake inhibition by venlafaxine in differentiated HT22 cells (IC_50, 27.21 nM) was comparable to that in HEK293 cells overexpressing serotonin transporter SERT (IC_50, 30.65 nM). These findings suggest that the differentiation of HT22 cells enhances their functional serotonergic properties, and these cells could be a potential in vitro system for assessing the efficacy of antidepressant 5-HT reuptake inhibitors.

In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters

New psychoactive substances (NPS) are a group of compounds that mimic the effects of illicit substances. A range of NPS have been shown to interact with the three main classes of monoamine transporters (DAT, NET, and SERT) to differing extents, but it is unclear why these differences arise. To aid in understanding the differences in affinity between the classes of monoamine transporters, several in silico experiments were conducted. Docking experiments showed there was no direct correlation between a range of scoring functions and experimental activity, but Spearman ranking analysis showed a significant correlation (α = 0.1) for DAT, with the affinity ΔG (0.42), αHB (0.40), GoldScore (0.40), and PLP (0.41) scoring functions, and for DAT (0.38) and SERT (0.40) using a consensus scoring approach. Qualitative structure-activity relationship (QSAR) experiments resulted in the generation of robust and predictive three-descriptor models for SERT (r ² = 0.87, q ² = 0.8, and test set r ² = 0.74) and DAT (r ² = 0.68, q ² = 0.51, test set r ² = 0.63). Both QSAR models described similar characteristics for binding, i.e., rigid hydrophobic molecules with a biogenic amine moiety, and were not sufficient to facilitate a deeper understanding of differences in affinity between the monoamine transporters. This contextualizes the observed promiscuity for NPS between the isoforms and highlights the difficulty in the design and development of compounds that are isoform-selective.

Application of In Silico Filtering and Isothermal Titration Calorimetry for the Discovery of Small Molecule Inhibitors of MDM2

The initial discovery phase of protein modulators, which consists of filtering molecular libraries and in vitro direct binding validation, is central in drug discovery. Thus, virtual screening of large molecular libraries, together with the evaluation of binding affinity by isothermal calorimetry, generates an efficient experimental setup. Herein, we applied virtual screening for discovering small molecule inhibitors of MDM2, a major negative regulator of the tumor suppressor p53, and thus a promising therapeutic target. A library of 20 million small molecules was screened against an averaged model derived from multiple structural conformations of MDM2 based on published structures. Selected molecules originating from the computational filtering were tested in vitro for their direct binding to MDM2 via isothermal titration calorimetry. Three new molecules, representing distinct chemical scaffolds, showed binding to MDM2. These were further evaluated by exploring structure-similar chemical analogues. Two scaffolds were further evaluated by de novo synthesis of molecules derived from the initial molecules that bound MDM2, one with a central oxoazetidine acetamide and one with benzene sulfonamide. Several molecules derived from these scaffolds increased wild-type p53 activity in MCF7 cancer cells. These set a basis for further chemical optimization and the development of new chemical entities as anticancer drugs.

Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design

Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least 2 decades in various fields of cheminformatics, from similarity searching to machine learning (ML). Advances in in silico techniques consequently led to combining both these methodologies into a new approach known as the pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., ECFP4, Estate, MACCS, PubChem, Substructure, Klekota-Roth, CDK, Extended, and GraphOnly) and the ChemAxon pharmacophoric features fingerprint. Pharmacoprint consisted of 39 973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of the bit string but also improved the efficiency of the ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for three-dimensional (3D) structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed us to maximize the Matthews correlation coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.

Synthesis and biological evaluation of 1-(4-(piperazin-1-yl)phenyl)pyridin-2(1H)-one derivatives as potential SSRIs

A series of novel 1-(4-(piperazin-1-yl)phenyl)pyridin-2(1H)-one derivatives were synthesized and evaluated for their serotonin (5-HT) reuptake inhibitory activity. The results in vitro indicated that most of the evaluated compounds displayed potent 5-HT reuptake inhibition. The most promising compound A20 was stable in human liver microsomes and possessed good pharmacokinetic properties. Antidepressant study in vivo of the compound A20 showed that A20 could potently antagonize the p-chloroamphetamine (PCA)-induced depletion of serotonin in hypothalamus and reduce immobility times in the rat forced swimming test (FST).

Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters

Background:

The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs.

Objective:

This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted.

Methods:

PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs.

Results:

Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models.

Conclusion:

The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.

An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs. activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs. Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of K_i from 10² to 10³ nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation

Dual inhibition of serotonin and norepinephrine transporters (hSERT and hNET) gives greatly improved efficacy and tolerability for treating major depressive disorder (MDD) compared with selective reuptake inhibitors. Pioneer studies provided valuable information on structure, function, and pharmacology of drugs targeting both hSERT and hNET (serotonin-norepinephrine reuptake inhibitors, SNRIs), and the differential binding mechanism between SNRIs and selective inhibitors of 5-HT (SSRIs) or NE (sNRIs) to their corresponding targets was expected to be able to facilitate the discovery of a privileged drug-like scaffold with improved efficacy. However, the dual-target mechanism of SNRIs was still elusive, and the binding mode distinguishing SNRIs from SSRIs and sNRIs was also unclear. Herein, an integrated computational strategy was adopted to discover the binding mode shared by all FDA approved SNRIs. The comparative analysis of binding free energy at the per-residue level discovered that residues Phe335, Leu337, Gly338, and Val343 located at the transmembrane domain 6 (TM6) of hSERT (the corresponding residues Phe317, Leu319, Gly320, and Val325 in hNET) were the determinants accounting for SNRIs' dual-acting inhibition, while residues lining TM3 and 8 (Ile172, Ser438, Thr439, and Leu443 in hSERT; Val148, Ser419, Ser420, and Met424 in hNET) contributed less to the binding of SNRIs than that of SSRIs and sNRIs. Based on these results, the distances between an SNRI's centroid and the centroids of its two aromatic rings (measuring the depth of rings stretching into hydrophobic pockets) were discovered as the key to the SNRIs' dual-targeting mechanism. This finding revealed SNRIs' binding mechanism at an atomistic level, which could be further utilized as structural blueprints for the rational design of privileged drug-like scaffolds treating MDD.

Structure and Dynamics Study of LeuT Using the Markov State Model and Perturbation Response Scanning Reveals Distinct Ion Induced Conformational States

The bacterial leucine transporter (LeuT), a close homologue of the eukaryote monoamine transporters (MATs), currently serves as a powerful template for computer simulations of MATs. Transport of the amino acid leucine through the membrane is made possible by the sodium electrochemical potential. Recent reports indicate that the substrate transport mechanism is based on structural changes such as hinge movements of key transmembrane domains. In order to further investigate the role of sodium ions in the uptake of leucine, here we present a Markov state model analysis of atomistic simulations of lipid embedded LeuT in different environments, generated by varying the presence of binding pocket sodium ions and substrate. Six metastable conformations are found, and structural differences between them along with transition probabilities are determined. We complete the analysis with the implementation of perturbation response scanning on our system, determining the most sensitive and influential regions of LeuT, in each environment. Our results show that the occupation of sites Na1 and Na2, along with the presence of the substrate, selectively influences the geometry of LeuT. In particular, the occupation of each site Na1/Na2 has strong effects (in terms of changes in influence and/or sensitivity, as compared to the case without ions) in specific regions of LeuT, and the effects are different for simultaneous occupation. Our results strengthen the rationale and provide a conformational mechanism for a putative transport mechanism in which Na2 is necessary, but may not be sufficient, to initiate and stabilize extracellular substrate access to the binding pocket.

Designing modulators of monoamine transporters using virtual screening techniques

The plasma-membrane monoamine transporters (MATs), including the serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters, serve a pivotal role in limiting monoamine-mediated neurotransmission through the reuptake of their respective monoamine neurotransmitters. The transporters are the main target of clinically used psychostimulants and antidepressants. Despite the availability of several potent and selective MAT substrates and inhibitors the continuing need for therapeutic drugs to treat brain disorders involving aberrant monoamine signaling provides a compelling reason to identify novel ways of targeting and modulating the MATs. Designing novel modulators of MAT function have been limited by the lack of three dimensional structure information of the individual MATs. However, crystal structures of LeuT, a bacterial homolog of MATs, in a substrate-bound occluded, substrate-free outward-open, and an apo inward-open state and also with competitive and non-competitive inhibitors have been determined. In addition, several structures of the Drosophila DAT have also been resolved. Together with computational modeling and experimental data gathered over the past decade, these structures have dramatically advanced our understanding of several aspects of SERT, NET, and DAT transporter function, including some of the molecular determinants of ligand interaction at orthosteric substrate and inhibitor binding pockets. In addition progress has been made in the understanding of how allosteric modulation of MAT function can be achieved. Here we will review all the efforts up to date that has been made through computational approaches employing structural models of MATs to design small molecule modulators to the orthosteric and allosteric sites using virtual screening techniques.

Discovery of novel-scaffold monoamine transporter ligands via in silico screening with the S1 pocket of the serotonin transporter

Discovery of new inhibitors of the plasmalemmal monoamine transporters (MATs) continues to provide pharmacotherapeutic options for depression, addiction, attention deficit disorders, psychosis, narcolepsy, and Parkinson’s disease. The windfall of high-resolution MAT structural information afforded by X-ray crystallography has enabled the construction of credible computational models. Elucidation of lead compounds, creation of compound structure–activity series, and pharmacologic testing are staggering expenses that could be reduced by using a MAT computational model for virtual screening (VS) of structural libraries containing millions of compounds. Here, VS of the PubChem small molecule structural database using the S1 (primary substrate) ligand pocket of a serotonin transporter homology model yielded 19 prominent “hit” compounds. In vitro pharmacology of these VS hits revealed four structurally unique MAT substrate uptake inhibitors with high nanomolar affinity at one or more of the three MATs. In vivo characterization of three of these hits revealed significant activity in a mouse model of acute depression at doses that did not elicit untoward locomotor effects. This constitutes the first report of MAT inhibitor discovery using exclusively the primary substrate pocket as a VS tool. Novel-scaffold MAT inhibitors offer hope of new medications that lack the many classic adverse effects of existing antidepressant drugs.

Identification of novel serotonin transporter compounds by virtual screening

The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) plays an essential role in the termination of serotonergic neurotransmission by removing 5-HT from the synaptic cleft into the presynaptic neuron. It is also of pharmacological importance being targeted by antidepressants and psychostimulant drugs. Here, five commercial databases containing approximately 3.24 million drug-like compounds have been screened using a combination of two-dimensional (2D) fingerprint-based and three-dimensional (3D) pharmacophore-based screening and flexible docking into multiple conformations of the binding pocket detected in an outward-open SERT homology model. Following virtual screening (VS), selected compounds were evaluated using in vitro screening and full binding assays and an in silico hit-to-lead (H2L) screening was performed to obtain analogues of the identified compounds. Using this multistep VS/H2L approach, 74 active compounds, 46 of which had K(i) values of ≤1000 nM, belonging to 16 structural classes, have been identified, and multiple compounds share no structural resemblance with known SERT binders.

A linear combination of pharmacophore hypotheses as a new tool in search of new active compounds--an application for 5-HT1A receptor ligands

This study explores a new approach to pharmacophore screening involving the use of an optimized linear combination of models instead of a single hypothesis. The implementation and evaluation of the developed methodology are performed for a complete known chemical space of 5-HT_1AR ligands (3616 active compounds with K_i < 100 nM) acquired from the ChEMBL database. Clusters generated from three different methods were the basis for the individual pharmacophore hypotheses, which were assembled into optimal combinations to maximize the different coefficients, namely, MCC, accuracy and recall, to measure the screening performance. Various factors that influence filtering efficiency, including clustering methods, the composition of test sets (random, the most diverse and cluster population-dependent) and hit mode (the compound must fit at least one or two models from a final combination) were investigated. This method outmatched both single hypothesis and random linear combination approaches.

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

New design strategies for antidepressant drugs

INTRODUCTION

In spite of research efforts spanning six decades, the most prominent antidepressant drugs to date still carry several adverse effects, often serious enough to warrant discontinuation of the drug. Molecular mechanisms of depression are now better understood such that some of the specific receptors responsible can be targeted for activation or inhibition. This advance, coupled with the recent availability of crystal structures of relevant drug targets or their homologs, has opened the door for new antidepressant therapeutic compounds.

AREAS COVERED

The authors review the evolution of monoamine-based antidepressant drugs, up to the selective serotonin reuptake inhibitors (SSRIs). The authors discuss classic and contemporary antidepressant drug design strategies, with a focus on virtual screening and fragment-based drug design methods. Furthermore, they discuss the recent advancements in the understanding of the serotonin transporter (SERT) structure/function relationship in the context of recognition of SSRIs and outline a strategy for the use of computational approaches in producing new SSRI lead compounds.

EXPERT OPINION

The authors suggest that given the long-awaited availability of credible three-dimensional structures for the SERT and related monoamine transporter proteins, cutting-edge computational methods should be the linchpin of future drug discovery efforts regarding monoamine-based antidepressant lead compounds. Because these transporter inhibitors cause a ubiquitous increase in extraneuronal neurotransmitter levels leading to side and adverse therapeutic effects, the drug discovery should extend to appropriate manipulation of the 'downstream' receptors affected by the neurotransmitter boost. Efficient use of new computational strategies will accelerate the drug discovery process and reduce its economic burden.

Monoamine transporter structure, function, dynamics, and drug discovery: a computational perspective

With the breakthrough crystallization of the bacterial leucine transporter protein LeuT, the first available X-ray structure for the neurotransmitter/sodium symporter family, development of 3-D computational models is suddenly essential for structure-function studies on the plasmalemmal monoamine transporters (MATs). LeuT-based MAT models have been used to guide elucidation of substrate and inhibitor binding pockets, and molecular dynamics simulations using these models are providing insight into conformations involved in the substrate translocation cycle. With credible MAT models finally in hand, structure-based virtual screening for novel ligands is yielding lead compounds toward the development of new medications for psychostimulant dependence, attention deficit hyperactivity, depression, anxiety, schizophrenia, and other disorders associated with dopamine, norepinephrine, or serotonin dysregulation.

Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor

Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.