Synthesis and characterization of gold(I) thiolate derivatives and bimetallic complexes for HIV inhibition.

Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties. Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) and Coetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1-11) were conducted using micro-analysis, NMR, and infrared spectrometry. Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS.

Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits.

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv .Scientific contributionWe propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.

Dendrimers as drug delivery systems for oncotherapy: Current status of promising applications.

Cancer affects millions of people worldwide, causing death and serious health problems. Despite significant investment in the development of new anticancer compounds, there are still several limitations that can still be found. Many compounds exhibit high levels of toxicity and low bioavailability. Therefore, it is urgent to design safer, more effective, and particularly more selective compounds for oncological treatment. Dendrimers are polymeric structures that have been shown to be potential drug nanocarriers to overcome physicochemical, pharmacokinetic, and indirect pharmacodynamic issues. Due to their versatility, they can be used in the design of nanovaccines, lipophilic complexes, amphiphilic complexes, smart nanocomplexes, and others. This work targets the use of dendrimers in oncological treatment and their importance and effectiveness as drug delivery systems for the development of new therapies. For this review, only publications from the last two years are considered in this review.

Navigating the frontier of drug-like chemical space with cutting-edge generative AI models.

Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS exploration: recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows (NF), and transformers. It discusses molecular representation choices, training strategies for focused CS exploration, evaluation criteria for CS coverage, and related challenges. Future directions include refining models, exploring new notations, improving benchmarks, and enhancing interpretability to better understand biologically relevant molecular properties.

In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

New potent EV-A71 antivirals targeting capsid.

The enterovirus is a genus of single-stranded, highly diverse positive-sense RNA viruses, including Human Enterovirus A-D and Human Rhinovirus A-C species. They are responsible for numerous diseases and some infections can progress to life-threatening complications, particularly in children or immunocompromised patients. To date, there is no treatment against enteroviruses on the market, except for polioviruses (vaccine) and EV-A71 (vaccine in China). Following a decrease in enterovirus infections during and shortly after the (SARS-Cov2) lockdown, enterovirus outbreaks were once again detected, notably in young children. This reemergence highlights on the need to develop broad-spectrum treatment against enteroviruses. Over the last year, our research team has identified a new class of small-molecule inhibitors showing anti-EV activity. Targeting the well-known hydrophobic pocket in the viral capsid, these compounds show micromolar activity against EV-A71 and a high selectivity index (SI) (5h: EC50, MRC-5 = 0.57 µM, CC50, MRC-5 >20 µM, SI > 35; EC50, RD = 4.38 µM, CC50, RD > 40 µM, SI > 9; 6c: EC50, MRC-5 = 0.29 µM, CC50, MRC-5 >20 µM, SI > 69; EC50, RD = 1.66 µM, CC50, RD > 40 µM, SI > 24; Reference: Vapendavir EC50, MRC-5 = 0.36 µM, CC50, MRC-5 > 20 µM, EC50, RD = 0.53 µM, CC50, RD > 40 µM, SI > 63). The binding mode of these compounds in complex with enterovirus capsids was analyzed and showed a series of conserved interactions. Consequently, 6c and its derivatives are promising candidates for the treatment of enterovirus infections.

A comprehensive review of new small molecule drugs approved by the FDA in 2022: Advance and prospect.

In 2022, the U.S. Food and Drug Administration approved a total of 16 marketing applications for small molecule drugs, which not only provided dominant scaffolds but also introduced novel mechanisms of action and clinical indications. The successful cases provide valuable information for optimizing efficacy and enhancing pharmacokinetic properties through strategies like macrocyclization, bioequivalent group utilization, prodrug synthesis, and conformation restriction. Therefore, gaining an in-depth understanding of the design principles and strategies underlying these drugs will greatly facilitate the development of new therapeutic agents. This review focuses on the research and development process of these newly approved small molecule drugs including drug design, structural modification, and improvement of pharmacokinetic properties to inspire future research in this field.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; and the development trend of new drug development for AD in the future.

Advances in structure-based drug design targeting membrane protein markers in prostate cancer.

Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.

Recent contributions of pyridazine as a privileged scaffold of anticancer agents in medicinal chemistry: An updated review.

Pyridazine, as a privileged scaffold, has been extensively utilized in drug development due to its multiple biological activities. Especially around its distinctive anticancer property, a massive number of pyridazine-containing compounds have been synthesized and evaluated that target a diverse array of biological processes involved in cancer onset and progression. These include glutaminase 1 (GLS1) inhibitors, tropomyosin receptor kinase (TRK) inhibitors, and bromodomain containing protein (BRD) inhibitors, targeting aberrant tumor metabolism, cell signal transduction and epigenetic modifications, respectively. Pyridazine moieties functioned as either core frameworks or warheads in the above agents, exhibiting promising potential in cancer treatment. Therefore, the review aims to summarize the recent contributions of pyridazine derivatives as potent anticancer agents between 2020 and 2024, focusing mainly on their structure-activity relationships (SARs) and development strategies, with a view to show that the application of the pyridazine scaffold by different medicinal chemists provides new insights into the rational design of anticancer drugs.

PDE4D: A Multipurpose Pharmacological Target.

Phosphodiesterase 4 (PDE4) enzymes catalyze cyclic adenosine monophosphate (cAMP) hydrolysis and are involved in a variety of physiological processes, including brain function, monocyte and macrophage activation, and neutrophil infiltration. Among different PDE4 isoforms, Phosphodiesterases 4D (PDE4Ds) play a fundamental role in cognitive, learning and memory consolidation processes and cancer development. Selective PDE4D inhibitors (PDE4Dis) could represent an innovative and valid therapeutic strategy for the treatment of various neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's, and Lou Gehrig's diseases, but also for stroke, traumatic brain and spinal cord injury, mild cognitive impairment, and all demyelinating diseases such as multiple sclerosis. In addition, small molecules able to block PDE4D isoforms have been recently studied for the treatment of specific cancer types, particularly hepatocellular carcinoma and breast cancer. This review overviews the PDE4DIsso far identified and provides useful information, from a medicinal chemistry point of view, for the development of a novel series of compounds with improved pharmacological properties.

Computational Methods for the Discovery and Optimization of TAAR1 and TAAR5 Ligands.

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

Advancements in the Research of New Modulators of Nitric Oxide Synthases Activity.

Nitric oxide (NO) has been defined as the "miracle molecule" due to its essential pleiotropic role in living systems. Besides its implications in physiologic functions, it is also involved in the development of several disease states, and understanding this ambivalence is crucial for medicinal chemists to develop therapeutic strategies that regulate NO production without compromising its beneficial functions in cell physiology. Although nitric oxide synthase (NOS), i.e., the enzyme deputed to the NO biosynthesis, is a well-recognized druggable target to regulate NO bioavailability, some issues have emerged during the past decades, limiting the progress of NOS modulators in clinical trials. In the present review, we discuss the most promising advancements in the research of small molecules that are able to regulate NOS activity with improved pharmacodynamic and pharmacokinetic profiles, providing an updated framework of this research field that could be useful for the design and development of new NOS modulators.

Aminopyridines in the development of drug candidates against protozoan neglected tropical diseases.

Neglected tropical diseases (NTDs) pose a major threat in tropical zones for impoverished populations. Difficulty of access, adverse effects or low efficacy limit the use of current therapeutic options. Therefore, development of new drugs against NTDs is a necessity. Compounds containing an aminopyridine (AP) moiety are of great interest for the design of new anti-NTD drugs due to their intrinsic properties compared with their closest chemical structures. Currently, over 40 compounds with an AP moiety are on the market, but none is used against NTDs despite active research on APs. The aim of this review is to present the medicinal chemistry work carried out with these scaffolds, against protozoan NTDs: Trypanosoma cruzi, Trypanosoma brucei or Leishmania spp.

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Understanding predictions of drug profiles using explainable machine learning models.

PURPOSE: The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug's effectiveness at its target location. This study concerns their prediction, using explainable Machine Learning (ML) models. The aim of the study is to find which molecular features are relevant to the prediction of the different ADME properties and measure their impact on the predictive model. METHODS: The relative relevance of individual features for ADME activity is gauged by estimating feature importance in ML models' predictions. Feature importance is calculated using feature permutation and the individual impact of features is measured by SHAP additive explanations. RESULTS: The study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction. CONCLUSION: The reported research illustrates how explainable ML models can provide detailed insights about the individual contributions of molecular features to the final prediction of an ADME property, as an effort to support experts in the process of drug candidate selection through a better understanding of the impact of molecular features.

Targeting Monkeypox Virus Methyltransferase: Virtual Screening of Natural Compounds from Middle-Eastern Medicinal Plants.

Monkeypox is an infectious disease resulting from the monkeypox virus, and its fatality rate varies depending on the virus clade and the location of the outbreak. In monkeypox virus, methyltransferase (MTase) plays a crucial role in modifying the cap structure of viral mRNA. This alteration assists the virus in evading the host's immune system, enhances viral protein synthesis, and ultimately enables successful infection and replication within host cells. Given the significance of MTase in viral infection and spread within the host, our study aimed to identify a natural inhibitor for this enzyme using docking and molecular dynamic (MD) simulations. We collected a total of 12,971 natural compounds from 200 medicinal plants in the Middle East. After eliminating duplicate compounds, we had 5,749 unique ligand conformers, which we then subjected to high-throughput virtual screening against MTase. The most promising hits were further evaluated using the extra-precision (XP) tool. The affinity of these hits was also assessed by Prime-Molecular Mechanics/Generalized Born Surface Area (MMGBSA) tool. The analysis revealed that two standard controls (sinefungin and TO1119) and two Middle-Eastern compounds (folic acid and 1,2,4,6-tetragalloylglucose) exhibited the best XP docking scores. According to Prime MMGBSA calculations, the Middle-Eastern compounds showed higher affinities, with values of - 60.61 kcal/mol for 1,2,4,6-tetragalloylglucose and - 51.87 kcal/mol for folic acid, surpassing the controls (TO1119 at - 35.71 kcal/mol and sinefungin at - 31.51 kcal/mol). In the majority of Molecular dynamic (MD) simulations, folic acid exhibited demonstrated greater stability than sinefungin. Further investigation revealed that folic acid occupied a critical position in the active site of MTase, which reduced its interaction with the mRNA substrate. Based on these findings, it can be concluded that folic acid is a highly promising natural compound for potential use in the cost-effective treatment of monkeypox virus. The identification of folic acid as a potential antiviral agent highlights the importance of nature in providing new therapeutic uses that have significant implications for global health, particularly in regions where monkeypox viral outbreaks are prevalent. However, it is essential to note that further wet-lab validations are necessary to confirm its efficacy for treatment in a medical context.

Application progress of deep generative models in de novo drug design.

The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.

Prediction of Protein-Drug Interactions, Pharmacophore Modeling, and Toxicokinetics of Novel Leads for Type 2 Diabetes Treatment.

BACKGROUND: Small heterocyclic compounds have been crucial in pioneering advances in type 2 diabetes treatment. There has been a dramatic increase in the pharmacological development of novel heterocyclic derivatives aimed at stimulating the activation of Glucokinase (GK). A pharmaceutical intervention for diabetes is increasingly targeting GK as a legitimate target. Diabetes type 2 compromises Glucokinase's function, an enzyme vital for maintaining the balance of blood glucose levels. Medicinal substances strategically positioned to improve type 2 diabetes management are used to stimulate the GK enzyme using heterocyclic derivatives. OBJECTIVE: The research endeavor aimed to craft novel compounds, drawing inspiration from the inherent coumarin nucleus found in nature. The goal was to evoke the activity of the glucokinase enzyme, offering a tailored approach to mitigate the undesired side effects typically associated with conventional therapies employed in the treatment of type 2 diabetes. METHODS: Coumarin, sourced from nature's embrace, unfolds as a potent and naturally derived ally in the quest for innovative antidiabetic interventions. Coumarin was extracted from a variety of botanical origins, including Artemisia keiskeana, Mallotus resinosus, Jatropha integerrima, Ferula tingitana, Zanthoxylum schinifolium, Phebalium clavatum, and Mammea siamensis. This inclusive evaluation was conducted on Muybridge's digital database containing 53,000 hit compounds. The presence of the coumarin nucleus was found in 100 compounds, that were selected from this extensive repository. Utilizing Auto Dock Vina 1.5.6 and ChemBioDraw Ultra, structures generated through this process underwent docking analysis. Furthermore, these compounds were accurately predicted online log P using the Swiss ADME algorithm. A predictive analysis was conducted using PKCSM software on the primary compounds to assess potential toxicity. RESULTS: Using Auto Dock Vina 1.5.6, 100 coumarin derivatives were assessed for docking. Glucokinase (GK) binding was significantly enhanced by most of these compounds. Based on superior binding characteristics compared with Dorzagliatin (standard GKA) and MRK (co-crystallized ligand), the top eight molecules were identified. After further evaluation through ADMET analysis of these eight promising candidates, it was confirmed that they met the Lipinski rule of five and their pharmacokinetic profile was enhanced. The highest binding affinity was demonstrated by APV16 at -10.6 kcal/mol. A comparison between the APV16, Dorzagliatin and MRK in terms of toxicity predictions using PKCSM indicated that the former exhibited less skin sensitization, AMES toxicity, and hepatotoxicity. CONCLUSION: Glucokinase is most potently activated by 100 of the compound leads in the database of 53,000 compounds that contain the coumarin nucleus. APV12, with its high binding affinity, favorable ADMET (adjusted drug metabolic equivalents), minimal toxicity, and favorable pharmacokinetic profile warrants consideration for progress to in vitro testing. Nevertheless, to uncover potential therapeutic implications, particularly in the context of type 2 diabetes, thorough investigations and in-vivo evaluations are necessary for benchmarking before therapeutic use, especially experiments involving the STZ diabetic rat model.

Comparative in silico analysis of CNS-active molecules targeting the blood-brain barrier choline transporter for Alzheimer's disease therapy.

This study investigated the blood‒brain barrier (BBB) permeability of the central nervous system (CNS)-active compounds donepezil (DON), methionine (MET), and memantine (MEM) by employing a comprehensive in silico approach. These compounds are of particular interest for Alzheimer's disease (AD) therapy. Rigid-flexible molecular docking simulations indicated favorable binding affinities of all the compounds with BBB-ChT, with DON exhibiting the highest binding affinity (ΔGbind = -10.26 kcal/mol), predominantly mediated by significant hydrophobic interactions. In silico kinetic profiling suggested the stability of the DON/BBB-ChT complex, with ligand release prompted by conformational changes. 3D molecular alignment corroborated a minor conformational shift for DON in its minimal binding energy pose. Predictions indicated that active transport mechanisms notably enhance the brain distribution of donepezil compared to that of MET and MEM. Additionally, DON and MEM exhibited low mutagenic probabilities, while MET was identified as highly mutagenic. Overall, these findings highlight the potential of donepezil for superior BBB penetration, primarily through active transport mechanisms, underscoring the need for further validation through in vitro and in vivo studies for effective AD treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00245-w.

Macrocyclic-based strategy in drug design: From lab to the clinic.

Macrocyclic compounds have emerged as potent tools in the field of drug design, offering unique advantages for enhancing molecular recognition, improving pharmacokinetic properties, and expanding the chemical space accessible to medicinal chemists. This review delves into the evolutionary trajectory of macrocyclic-based strategies, tracing their journey from laboratory innovations to clinical applications. Beginning with an exploration of the defining structural features of macrocycles and their impact on drug-like characteristics, this discussion progresses to highlight key design principles that have facilitated the development of diverse macrocyclic drug candidates. Through a series of illustrative representative case studies from approved macrocyclic drugs and candidates spanning various therapeutic areas, particular emphasis is placed on their efficacy in targeting challenging protein-protein interactions, enzymes, and receptors. Additionally, this review thoroughly examines how macrocycles effectively address critical issues such as metabolic stability, oral bioavailability and selectivity. Valuable insights into optimization strategies employed during both approved and clinical phases underscore successful translation of promising leads into efficacious therapies while providing valuable perspectives on harnessing the full potential of macrocycles in drug discovery and development endeavors.