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.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins.

INTRODUCTION: Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules. AREA COVERED: This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors. EXPERT OPINION: The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.

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.

Medicinal chemistry aspects of fat mass and obesity associated protein: structure, function and inhibitors.

Adiposity and obesity-related proteins (FTO), the earliest identified mRNA N6-methyladenosine (m6A) demethylases, are known to play crucial roles in several biological processes. Therefore, FTO is a promising target for anticancer treatment. Understanding the biological functions and regulatory mechanisms of FTO targets can serve as guidelines for drug development. Despite significant efforts to develop FTO inhibitors, no specific small-molecule inhibitors have entered clinical trials so far. In this manuscript, we review the relationship between FTO and various cancers, the small-molecule inhibitors developed against FTO targets from the perspective of medicinal chemistry and other fields, and describe their structural optimization process and structure-activity relationship, providing clues for their future development direction.

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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.

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

BACKGROUND: Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS: This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS: This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS: The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION: Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

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.

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.

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.

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.

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.

Metis: a python-based user interface to collect expert feedback for generative chemistry models.

One challenge that current de novo drug design models face is a disparity between the user's expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists' implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback. Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists' detailed feedback on molecular structures. The GUI enables chemists to explore and evaluate molecules, offering a user-friendly interface for annotating preferences and specifying desired or undesired structural features. By providing chemists the opportunity to give detailed feedback, allows researchers to capture more efficiently the chemist's implicit knowledge and preferences. This knowledge is crucial to align the chemist's idea with the de novo design agents. The GUI aims to enhance this collaboration between the human and the "machine" by providing an intuitive platform where chemists can interactively provide feedback on molecular structures, aiding in preference learning and refining de novo design strategies. Metis integrates with the existing de novo framework REINVENT, creating a closed-loop system where human expertise can continuously inform and refine the generative models.Scientific contributionWe introduce a novel Graphical User Interface, that allows chemists/researchers to give detailed feedback on substructures and properties of small molecules. This tool can be used to learn the preferences of chemists in order to align de novo drug design models with the chemist's ideas. The GUI can be customized to fit different needs and projects and enables direct integration into de novo REINVENT runs. We believe that Metis can facilitate the discussion and development of novel ways to integrate human feedback that goes beyond binary decisions of liking or disliking a molecule.

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.

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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