Molecular modeling of the 3D structure of 5-HT(1A)R: discovery of novel 5-HT(1A)R agonists via dynamic pharmacophore-based virtual screening

MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning

De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.

Advances in drug design and therapeutic potential of selective or multitarget 5-HT1A receptor ligands

5-HT1A receptor (5-HT1A-R) is a serotoninergic G-protein coupled receptor subtype which contributes to several physiological processes in both central nervous system and periphery. Despite being the first 5-HT-R identified, cloned and studied, it still represents a very attractive target in drug discovery and continues to be the focus of a myriad of drug discovery campaigns due to its involvement in numerous neuropsychiatric disorders. The structure-activity relationship studies (SAR) performed over the last years have been devoted to three main goals: (i) design and synthesis of 5-HT1A-R selective/preferential ligands; (ii) identification of 5-HT1A-R biased agonists, differentiating pre- versus post-synaptic agonism and signaling cellular mechanisms; (iii) development of multitarget compounds endowed with well-defined poly-pharmacological profiles targeting 5-HT1A-R along with other serotonin receptors, serotonin transporter (SERT), D2-like receptors and/or enzymes, such as acetylcholinesterase and phosphodiesterase, as a promising strategy for the management of complex psychiatric and neurodegenerative disorders. In this review, medicinal chemistry aspects of ligands acting as selective/preferential or multitarget 5-HT1A-R agonists and antagonists belonging to different chemotypes and developed in the last 7 years (2017-2023) have been discussed. The development of chemical and pharmacological 5-HT1A-R tools for molecular imaging have also been described. Finally, the pharmacological interest of 5-HT1A-R and the therapeutic potential of ligands targeting this receptor have been considered.

Molecular hybridization: a powerful tool for multitarget drug discovery

INTRODUCTION

The current drug discovery paradigm of 'one drug, multiple targets' has gained attention from both the academic medicinal chemistry community and the pharmaceutical industry. This is in response to the urgent need for effective agents to treat multifactorial chronic diseases. The molecular hybridization strategy is a useful tool that has been widely explored, particularly in the last two decades, for the design of multi-target drugs.

AREAS COVERED

This review examines the current state of molecular hybridization in guiding the discovery of multitarget small molecules. The article discusses the design strategies and target selection for a multitarget polypharmacology approach to treat various diseases, including cancer, Alzheimer's disease, cardiac arrhythmia, endometriosis, and inflammatory diseases.

EXPERT OPINION

Although the examples discussed highlight the importance of molecular hybridization for the discovery of multitarget bioactive compounds, it is notorious that the literature has focused on specific classes of targets. This may be due to a deep understanding of the pharmacophore features required for target binding, making targets such as histone deacetylases and cholinesterases frequent starting points. However, it is important to encourage the scientific community to explore diverse combinations of targets using the molecular hybridization strategy.

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

Recent advances in dopamine D2 receptor ligands in the treatment of neuropsychiatric disorders

Dopamine is a biologically active amine synthesized in the central and peripheral nervous system. This biogenic monoamine acts by activating five types of dopamine receptors (D_1-5 Rs), which belong to the G protein-coupled receptor family. Antagonists and partial agonists of D₂ Rs are used to treat schizophrenia, Parkinson's disease, depression, and anxiety. The typical pharmacophore with high D₂ R affinity comprises four main areas, namely aromatic moiety, cyclic amine, central linker and aromatic/heteroaromatic lipophilic fragment. From the literature reviewed herein, we can conclude that 4-(2,3-dichlorophenyl), 4-(2-methoxyphenyl)-, 4-(benzo[b]thiophen-4-yl)-1-substituted piperazine, and 4-(6-fluorobenzo[d]isoxazol-3-yl)piperidine moieties are critical for high D₂ R affinity. Four to six atoms chains are optimal for D₂ R affinity with 4-butoxyl as the most pronounced one. The bicyclic aromatic/heteroaromatic systems are most frequently occurring as lipophilic appendages to retain high D₂ R affinity. In this review, we provide a thorough overview of the therapeutic potential of D₂ R modulators in the treatment of the aforementioned disorders. In addition, this review summarizes current knowledge about these diseases, with a focus on the dopaminergic pathway underlying these pathologies. Major attention is paid to the structure, function, and pharmacology of novel D₂ R ligands, which have been developed in the last decade (2010-2021), and belong to the 1,4-disubstituted aromatic cyclic amine group. Due to the abundance of data, allosteric D₂ R ligands and D₂ R modulators from patents are not discussed in this review.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

Discovery of Novel Indolealkylpiperazine Derivatives as Potent 5-HT1A Receptor Agonists for the Potential Future Treatment of Depression

Depression is a severe psychiatric disorder that affects over 100 million people worldwide. 5-HT_1A receptor agonists have been implicated in the treatment of a variety of central nervous system diseases, especially depression. In this study, based on FW01, a selective potent 5-HT_1AR agonist discovered via dynamic pharmacophore-based virtual screening, a series of indolealkylpiperazine derivatives with a benzamide moiety were designed and synthesized by the modification of the amide tail group as well as indole head group of FW01. Among all tested compounds, 13m displayed potent agonistic activity towards 5-HT_1AR with an EC₅₀ value of 1.01 nM. Molecular docking studies were performed to disclose the mechanism of its potent agonistic activity and high selectivity. Finally, the activation model of 5-HT_1AR induced by 13m was proposed.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Design, Synthesis, and Structure-Activity Relationship Studies of Novel Indolyalkylpiperazine Derivatives as Selective 5-HT1A Receptor Agonists

5-HT_1A receptor (5-HT_1AR) agonists have been implicated in the treatment of a variety of central nervous system (CNS) diseases such as depression and anxiety, et al. Based on our previously found compound FW01 (K_i = 51 ± 16 nM) obtained by virtual screening, a series of FW01 derivatives were designed and synthesized by the modification of the amide tail group as well as indole headgroup of FW01. SAR exploration found that amide tail group and indole headgroup play pivotal roles in determining the binding affinity and selectivity on dopamine and serotonin receptor subtypes. Among all tested compounds, 9_24 has a K_i value of 5 ± 0.6 nM with a good selectivity toward 5-HT_1AR. The [³⁵S] GTPγS assay showed that 9_24 is a full agonist toward 5-HT_1AR with an EC₅₀ value of 0.059 nM, which shows 266.2 and 146.4-fold selectivity to 5-HT_2A and D₃ respectively. Molecular dynamics simulations and molecular docking studies with 5-HT_1AR-9_24 were performed to disclose the mechanism of its high activity and selectivity. Finally, a detailed stepwise 9_24 induced signal transduction mechanism of 5-HT_1AR is proposed.

Virtual screening-guided discovery of thioredoxin reductase inhibitors

The selenoprotein thioredoxin reductase (TXNRD) is a promising therapeutic target for cancer. To discover novel TXNRD inhibitors, a library of α, β-unsaturated carbonyl compounds were applied in structure-based virtual screening for the selection of hit compounds. Fifteen top-ranked compounds were further validated experimentally, exhibiting potent inhibition of TXNRD and remarkable cytotoxicity to cancer cells. The further binding mode analysis indicated that multiple noncovalent interactions between the inhibitors and the active pocket of TXNRD facilitated the formation of covalent bonds between the Sec498 on TXNRD and the α, β-unsaturated carbonyl groups on inhibitors. Results from both simulations and experiments demonstrated that Sec498 is the prime interaction site for the inhibition of TXNRD. Taking compound 7 as an example, the inhibition of TXNRD by compounds promoted oxidative stress-mediated apoptosis of cancer cells. Given these findings, novel TXNRD inhibitors may be discovered and introduced to the growing fields of small molecule drugs against TXNRD.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

The Pharmacological Heterogeneity of Nepenthone Analogs in Conferring Highly Selective and Potent κ-Opioid Agonistic Activities

To develop novel analgesics with no side effects or less side effects than traditional opioids is highly demanded to treat opioid receptor mediated pain and addiction issues. Recently, κ-opioid receptor (KOR) has been established as an attractive target, although its selective agonists could bear heterogeneous pharmacological activities. In this study, we designed and synthesized two new series of nepenthone derivatives by inserting a spacer (carbonyl) between 6α,14α-endo-ethenylthebaine and the 7α-phenyl substitution of the skeleton and by substituting the 17-N-methyl group with a cyclopropylmethyl group. We performed in vitro tests (binding and functional assays) and molecular docking operations on our newly designed compounds. The results of wet-experimental measures and modeled binding structures demonstrate that these new compounds are selective KOR agonists with nanomolar level affinities. Compound 4 from these new derivatives showed the highest affinity (K_i = 0.4 ± 0.1 nM) and the highest selectivity (μ/κ = 339, δ/κ = 2034) toward KOR. The in vivo tests revealed that compound 4 is able to induce stronger (ED₅₀ = 2.1 mg/kg) and much longer antinociceptive effect than that of the typical KOR agonist U50488H (ED₅₀ = 4.4 mg/kg). Therefore, compound 4 can be used as a perfect lead compound for future design of potent analgesics acting through KOR.

In silico Analysis and Experimental Validation of Lignan Extracts from Kadsura longipedunculata for Potential 5-HT1AR Agonists

Objectives

Kadsura longipedunculata (KL) has been widely used for the treatment of insomnia in traditional Chinese medicine. The aim of this study was to explore the mechanism of the sedative and hypnotic effects of KL.

Materials and Methods

The content of KL was evaluated by HPLC-TOF-MS, and a potential target was found and used to construct its 3D structure to screen for potential ligands among the compounds in KL by using bioinformatics analysis, including similarity ensemble approach (SEA) docking, homology modeling, molecular docking and ligand-based pharmacophore. The PCPA-induced insomnia rat model was then applied to confirm the potential targets related to the sedative effects of KL by performing the forced swimming test (FST), the tail suspension test (TST) and the measurement of target-related proteins using western blotting and immunofluorescence.

Results

Bioinformatics analysis showed that most of lignan compounds in KL were optimal ligands for the 5-HT1A receptor (5-HT1AR), and they were found to be potential targets related to sedative effects; the main lignan content of KL extracts was characterized by HPLC-TOF-MS, with 7 proposed lignans detected. Administration of KL could significantly reduce FST and TST immobility time in the PCPA-induced 5HT-depleted insomnia rat model. The expressions of proteins related to the 5-HT1AR pathway were regulated by extracts of KL in a concentration-dependent manner, indicating that extracts of KL had 5-HT1AR agonist-like effects.

Conclusion

In silico analysis and experimental validation together demonstrated that lignan extracts from KL can target 5-HT1AR in insomniac rats, which could shed light on its use as a potential 5-HT1AR agonist drug.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.