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The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

Diastereoselective (3+3) cycloaddition between bicyclobutanes and pyridinium ylides forms azabicyclo[3.1.1]heptanes via pyridine dearomatization. These reactions proceed under ambient conditions with no need for photochemistry or catalysis, and tolerate a wide range of functional gorups. The resulting multicyclic ring systems have diverse synthetic handles for further transformations, making them potentially valuable for the design of Csp³-rich drug candidates. These include semi-reduction of the dihydropyridine, and diastereoselective photochemical skeletal rearrangement to give a tetrasubstituted cyclobutane. [ABSTRACT FROM AUTHOR]

Monocyclic 5‐membered heterocycles including imidazoles, thiazoles, oxazoles, and their related compounds have gained significant attention in medicinal chemistry because of their potent anticancerous activity. These small heterocyclic molecules possess versatile properties, including biological activity, absorption, distribution, metabolism, excretion, and chemical diversity that give them immense potential as anticancer agents. It is also a fact that inherent characteristic of azoles to combine with many biological molecules through hydrogen bond, stacking, and hydrophobic interaction makes them effective against almost all cancer types. In the present paper the author discusses the way which is connected with chemical structure of monocyclic azoles and their anticancer activity namely the ability of these compounds to intercalate with DNA, to inhibit some enzymes and to interfere cellular signaling pathways. Interestingly, several azole derivatives have been seen to be effective in preclinical efficacy studies as well as in clinical trials and are considered to be potent in overcoming the problem of resistance and side effects of the common anticancer agents. As the synthetic chemistry progresses, the structural system of the azoles has diversified and development in the pharmacology has become more specific. This has helped in enhancing the formation of new molecules in the azole class with improved selectivity and efficacy. Furthermore, the comprehensive review explains how computational chemistry and structure‐activity relationship (SAR) approaches are applied to the design of future‐generation azole compounds. In light of these facts, this article is designed to give a broad overview of the current state of monocyclic azole‐based anticancer agents in an attempt to further assert its therapeutic promise and spur further attempts at infusing the said agents into the cancer therapeutics fray. The discoveries made in this study may allow the development the radical different therapeutic approaches, which could lead to improved and targeted treatment of cancer. [ABSTRACT FROM AUTHOR]

Target-first drug discovery relies heavily on protein structure information, which severely limits its application. In recent years, fragment-based drug Design (FBDD) has been identified as an alternative solution, where screening of smaller molecules for lower affinity allowed the use of focused libraries with a higher hit rate. It is shown that coupling an sp2-rich heteroaromatic group with a monofunctional sp3-rich core gives fragments (186 examples) with advantageous physical-chemical properties, covering a chemical space often neglected in traditional libraries. [ABSTRACT FROM AUTHOR]

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB. Scientific contribution: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinase 9 (CDK9) and cytochrome P450 3A4 (CYP3A4) have emerged as promising targets in the development of anticancer drugs, presenting a consistent challenge in the quest for potent inhibitors. CDK9 inhibitors can selectively target fast-growing cancer cells by disrupting transcription elongation, which in turn hinders the production of proteins essential for cell cycle progression and survivaŚ. Understanding how CYP3A4 metabolizes specific chemotherapy drugs allows for personalized treatment plans, optimizing drug dosages according to a patient's metabolic profile. Since many cancer patients undergo combination therapies, and CYP3A4 is vital in drug metabolism, its inhibition or induction by one drug can alter the plasma levels of others, potentially leading to treatment failure or increased toxicity. Therefore, managing CYP3A4 activity is critical for effective cancer treatment. Employing a range of computational methodologies, this study systematically investigated the binding mechanisms of pyrimidine derivatives against CDK9 and CYP3A4. The field-based model demonstrated high R 2 values (0.99), with Q2 (0.66), demonstrating its ability to predict in silico inhibitory activity against the target of this study. The screening process followed in this work led to the discovery of powerful new inhibitor compounds. Of the 15 new compounds designed, three have a high affinity with the target (ranging from −8 to −9 kcal/mol kcal/mol) and were singled out through docking filtration for more detailed investigation. As well as, a reference compound with a substantial pIC50 value of 8.4, serving as the foundation for the development of the new compounds, was included for comparative analysis. To elucidate the essential features of CDK9 and CYP3A4 inhibitor design, a comparative analysis was conducted between 3D-QSAR-generated contours and molecular docking conformations of ligands. Molecular dynamics simulations were carried out for a duration of 100 ns on selected docked complexes, specifically those involving novel compounds with CDK9 and CYP3A4 enzymes. Additionally, the binding free energy for these complexes was assessed using the MM/PBSA method, which evaluates the free energy landscape of protein-ligand interactions. The results of MM/PBSA highlighted the strength of the new compounds in enhancing interactions with the target protein, which favors the results of molecular docking and MD simulation. These insights contribute to a deeper understanding of the mechanisms underlying CDK9 and CYP3A4 inhibition, offering potential avenues for the development of innovative and effective CDK9 inhibitors. [ABSTRACT FROM AUTHOR]

Lipinski's Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease(AD) is the most common type of dementia. It is a progressive disease beginning with mild memory loss and possibly leading to loss of the ability to carry on a conversation and respond to the environment. This study investigates the relationship between the chemical structure of potential AD drugs and their therapeutic efficacy using Multi-Criteria Decision-Making (MCDM) techniques including The approach for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) method. A comprehensive dataset comprising molecular descriptors and corresponding pharmacological properties, i.e., melting point, boiling point, molecular weight and density of AD drugs was compiled from diverse sources. Topological indices were calculated to capture the structural characteristics of these compounds. Application of TOPSIS and SAW through Entropy method helps obtain optimal drugs for curing AD. Quantitative Structure Property Relationships (QSPR) analysis has been done between properties and topological indices of AD's drug structures. Results revealed significant relations between specific topological indices and drug efficacy, providing insights into the structural features crucial for AD treatment efficacy. This approach offers a promising avenue for rational drug design and optimization in the quest for novel AD therapeutics. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IDD) is one of the most frequent causes of low back pain. No treatment is currently available to delay the progression of IDD. Conservative treatment or surgical interventions is only used to target the symptoms of IDD rather than treat the underlying cause. Currently, numerous potential therapeutic strategies are available, including molecular therapy, gene therapy, and cell therapy. However, the hostile environment of degenerated discs is a major problem that has hindered the clinical applicability of such approaches. In this regard, the design of drugs using alternative delivery systems (macro-, micro-, and nano-sized particles) may resolve this problem. These can protect and deliver biomolecules along with helping to improve the therapeutic effect of drugs via concentrating, protecting, and prolonging their presence in the degenerated disc. This review summarizes the research progress of diagnosis and the current options for treating IDD. [ABSTRACT FROM AUTHOR]

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Inflammation and immune dysregulation have been linked to the pathogenesis and progression of Parkinson's disease (PD), and represent an attractive target for therapeutic intervention, given the potential for repurposing of existing anti-inflammatory and immunomodulatory agents. Despite the fact that initial studies of drugs with secondary anti-inflammatory effects did not yield positive results, agents specifically targeting immune and inflammatory pathways may hold more promise. This article will briefly review the evidence base for targeting the immune system and neuroinflammation in PD, and discuss in detail the recently completed and currently active trials of primary anti-inflammatory/immunomodulatory drugs in PD. Plain Language Summary: Parkinson's disease is caused by a loss of dopamine-producing nerve cells in the brain. Recent research has suggested that activation of the immune system, leading to inflammation in the brain and body, can contribute to this loss. Current medications that are used to treat Parkinson's disease only help with the symptoms, and do not slow down the damage to nerve cells in the brain. New treatments, aiming to reduce inflammation and thereby slow disease progression, are under investigation in a number of clinical trials which are reviewed in this article. These treatments include medications that have been used in other diseases, as well as new drugs designed to target inflammation and immune activation in PD. Some of these early studies have had encouraging results but further larger trials are needed to determine whether medications targeting inflammation will have benefit for people with PD. [ABSTRACT FROM AUTHOR]

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three‐dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA‐targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA‐targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA‐binding small molecules. We focus on recent advances in developing RNA‐targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early‐stage discovery of RNA‐binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future. [ABSTRACT FROM AUTHOR]

Histone deacetylase 6 (HDAC6) was a potential target for Alzheimer's disease (AD). In this study, a series of novel oxyevodiamine-based HDAC6 inhibitors with a variety of linker moieties were designed, synthesized and evaluated. Compound 12 with a benzyl linker was identified as a high potent and selective HDAC6 inhibitor. It inhibited HDAC6 with an IC50 value of 6.2 nM and was more than 200 fold selectivity over HDAC1. It also had lower cytotoxicity and higher anti-H2O2 activity in vitro comparing with other derivatives. Compound 12 might be a good lead as novel HDAC6 inhibitor for the treatment of AD. [ABSTRACT FROM AUTHOR]

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development. PLAIN LANGUAGE SUMMARY: The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design. [ABSTRACT FROM AUTHOR]

In recent decades, nanotechnology has significantly advanced drug delivery systems, particularly in targeting subcellular organelles, thus opening new avenues for disease treatment. Mitochondria, critical for cellular energy and health, when dysfunctional, contribute to cancer, neurodegenerative diseases, and metabolic disorders. This has propelled the development of nanomedicines aimed at precise mitochondrial targeting to modulate their function, marking a research hotspot. This review delves into the recent advancements in mitochondrial-targeted nanotherapeutics, with a comprehensive focus on targeting strategies, nanocarrier designs, and their therapeutic applications. It emphasizes nanotechnology’s role in enhancing drug delivery by overcoming biological barriers and optimizing drug design for specific mitochondrial targeting. Strategies exploiting mitochondrial membrane potential differences and specific targeting ligands improve the delivery and mitochondrial accumulation of nanomedicines. The use of diverse nanocarriers, including liposomes, polymer nanoparticles, and inorganic nanoparticles, tailored for effective mitochondrial targeting, shows promise in anti-tumor and neurodegenerative treatments. The review addresses the challenges and future directions in mitochondrial targeting nanotherapy, highlighting the need for precision, reduced toxicity, and clinical validation. Mitochondrial targeting nanotherapy stands at the forefront of therapeutic strategies, offering innovative treatment perspectives. Ongoing innovation and research are crucial for developing more precise and effective treatment modalities. [ABSTRACT FROM AUTHOR]

Neurotrophin receptors of the Trk family are involved in the regulation of brain development and neuroplasticity, and therefore can serve as targets for anti-cancer and stroke-recovery drugs, antidepressants, and many others. The structures of Trk protein domains in various states upon activation need to be elucidated to allow rational drug design. However, little is known about the conformations of the transmembrane and juxtamembrane domains of Trk receptors. In the present study, we employ NMR spectroscopy to solve the structure of the TrkB dimeric transmembrane domain in the lipid environment. We verify the structure using mutagenesis and confirm that the conformation corresponds to the active state of the receptor. Subsequent study of TrkB interaction with the antidepressant drug fluoxetine, and the antipsychotic drug chlorpromazine, provides a clear self-consistent model, describing the mechanism by which fluoxetine activates the receptor by binding to its transmembrane domain. Neurotrophin receptor TrkB regulates neuronal growth and neuroplasticity. Here, the authors present the NMR structure of the intramembrane region of TrkB activated by antidepressant drugs, yielding insights into receptor function. [ABSTRACT FROM AUTHOR]

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field. Generative AI holds promise for creating novel compounds. Here, authors introduce TamGen, a GPT-like model designed to generate molecules tailored to specific target proteins. TamGen identified 14 potent compounds against the Tuberculosis ClpP protease, showing its potential for drug discovery. [ABSTRACT FROM AUTHOR]

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence‐driven drug discovery scenarios. However, current molecular representation models rarely consider the three‐dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi‐target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre‐trained on a relatively small‐scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero‐shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design. [ABSTRACT FROM AUTHOR]

Accurate prediction of protein‐ligand binding affinities is an essential challenge in structure‐based drug design. Despite recent advances in data‐driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein‐ligand complexes is curated, and Dynaformer, a graph‐based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein‐ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state‐of‐the‐art scoring and ranking power on the CASF‐2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process. [ABSTRACT FROM AUTHOR]

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson's correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost. Binding free energy calculations are crucial in computational drug discovery, however, the current alchemical free energy perturbation (FEP) requires large computational capabilities to achieve high accuracy. Here, the authors develop an alternative method by combining QM/MM and the mining minima method to predict free energies at lower computational cost and with comparable accuracy to FEP-based methods. [ABSTRACT FROM AUTHOR]

Lecithin-cholesterol acyltransferase (LCAT) serves as a pivotal enzyme in preserving cholesterol homeostasis via reverse cholesterol transport, a process closely associated with the onset of atherosclerosis. Impaired LCAT function can lead to severe LCAT deficiency disorders for which no pharmacological treatment exists. LCAT-based therapies, such as small molecule positive allosteric modulators (PAMs), against LCAT deficiencies and atherosclerosis hold promise, although their efficacy against atherosclerosis remains challenging. Herein we utilized a quantitative in silico metric to predict the activity of novel PAMs and tested their potencies with in vitro enzymatic assays. As predicted, sodium-glucose cotransporter 2 (SGLT2) inhibitors (gliflozins), sucrose and flavonoids activate LCAT. This has intriguing implications for the mechanism of action of gliflozins, which are commonly used in the treatment of type 2 diabetes, and for the endogenous activation of LCAT. Our results underscore the potential of molecular dynamics simulations in rational drug design. [ABSTRACT FROM AUTHOR]

The use of metal compounds such as cisplatin and its derivatives as therapeutic and diagnostic agents against various diseases is well established. Although metallodrugs have been very successful clinically, low therapeutic selectivity and the potential toxicity of the metal compounds mandate the need for a continuous search for more efficient and specific treatments. Hence, the use of computer‐aided drug design (CADD), molecular modelling and theoretical studies in designing, selecting and applying potential drug candidates have become ever more necessary. Among the many computational and theoretical techniques, molecular docking and density functional theory play significant roles in predicting drug activity. Recent methodologies, within the past 5 years, that rely on these techniques to advance the adoption of potential metallodrugs are highlighted and thoroughly discussed. This is because advancements in computing power have led to the wide use of CADD approaches for drug discovery, development and analysis to shorten the time and reduce the costs associated with drug discovery. [ABSTRACT FROM AUTHOR]

Mastering of analytical methods for accurate quantitative determinations of enantiomeric excess is a crucial aspect in asymmetric catalysis, chiral synthesis, and pharmaceutical applications. In this context, the phenomenon of Self‐Induced Diastereomeric Anisochronism (SIDA) can be exploited in NMR spectroscopy for accurate determinations of enantiomeric composition, without using a chiral auxiliary that could interfere with the spectroscopic investigation. This phenomenon can be particularly useful for improving the quantitative analysis of mixtures with low enantiomeric excesses, where direct integration of signals can be tricky. Here, we describe a novel analysis protocol to correctly determine the enantiomeric composition of scalemic mixtures and investigate the thermodynamic and stereochemical features at the basis of SIDA. Dipeptide derivatives were chosen as substrates for this study, given their central role in drug design. By integrating the experiments with a conformational stochastic search that includes entropic contributions, we provide valuable information on the dimerization thermodynamics, the nature of non‐covalent interactions leading to self‐association, and the differences in the chemical environment responsible for the anisochronism, highlighting the importance of different stereochemical arrangement and tight association for the distinction between homochiral and heterochiral adducts. An important role played by the counterion was pointed out by computational studies. [ABSTRACT FROM AUTHOR]

Quetiapine is a second-generation atypical antipsychotic drug that has been commonly prescribed for the treatment of schizophrenia, major depressive disorder (depression), and other psychological disorders. Targeted inhibition of hyperpolarization-activated cyclic-nucleotide gated (HCN) channels, which generate Ih, may provide effective resistance against schizophrenia and depression. We investigated if HCN channels could contribute to the therapeutic effect of quetiapine, and its major active metabolite norquetiapine. Two-electrode voltage clamp recordings were used to assess the effects of quetiapine and its active metabolites 7-hydroxyquetiapine and norquetiapine on currents from HCN1 channels expressed in Xenopus laevis oocytes. Norquetiapine, but not quetiapine nor 7-hydroxyquetiapine, has an inhibitory effect on HCN1 channels. Norquetiapine selectively inhibited HCN1 currents by shifting the voltage-dependence of activation to more hyperpolarized potentials in a concentration-dependent manner with an IC50 of 13.9 ± 0.8 µM for HCN1 and slowing channel opening, without changing the kinetics of closing. Inhibition by norquetiapine primarily occurs from in the closed state. Norquetiapine inhibition is not sensitive to the external potassium concentration, and therefore, likely does not block the pore. Norquetiapine inhibition also does not dependent on the cyclic-nucleotide binding domain. Norquetiapine also inhibited HCN4 channels with reduced efficacy than HCN1 and had no effect on HCN2 channels. Therefore, HCN channels are key targets of norquetiapine, the primary active metabolite of quetiapine. These data help to explain the therapeutic mechanisms by which quetiapine aids in the treatment of anxiety, major depressive disorder, bipolar disorder, and schizophrenia, and may represent a novel structure for future drug design of HCN inhibitors. [ABSTRACT FROM AUTHOR]

Antibacterial bone cements (ABCs), such as antibiotic-loaded bone cements (ALBCs), have been widely utilized in clinical treatments. Currently, bone cements loaded with vancomycin, gentamicin, tobramycin, or clindamycin are approved by the US Food and Drug Administration. However, traditional ALBCs exhibit drawbacks like burst release and bacterial resistance. Therefore, there is a demand for the development of antibacterial bone cements containing novel agents to address these defects. In this review, we provide an overview and prospect of the new antibacterial agents that can be used or have the potential to be applied in bone cement, including metallic antibacterial agents, pH-switchable antibacterial agents, cationic polymers, N-halamines, non-leaching acrylic monomers, antimicrobial peptides and enzymes. Additionally, we have conducted a preliminary assessment of the feasibility of bone cement containing N-halamine, which has demonstrated good antibacterial activities. The conclusion of this review is that the research and utilization of bone cement containing novel antibacterial agents contribute to addressing the limitations of ALBCs. Therefore, it is necessary to continue expanding the research and use of bone cement incorporating novel antibacterial agents. This review offers a novel perspectives for designing ABCs and treating bone infections. [ABSTRACT FROM AUTHOR]

Exploring the different spin states of central metals in the complex to regulate the anti-tumor activity of cancer cells is of great significance in drug design and clinical use. However, it is a challenge to build a strong coupling between spin states and anti-tumor activities in one system. Herein, we present two complexes {FeII2L2[PdII(CN)4]2}·2H2O (L = Bztpen (1), Bztppn (2); Bztpen = N-benzyl-N,N′,N′-tris(2-pyridylmethyl)ethylenediamine, Bztppn = N-benzyl-N,N′,N′-tris(2-pyridylmethyl)propylenediamine) showing different cytotoxic activities actuated by fine-tuning the structure with different spin states of Fe(II). Magnetic susceptibility measurements and X-ray diffraction revealed that the Fe(II) ion in complexes 1 and 2 remains in the LS and HS state, respectively, at room temperature. Cytotoxicity tests indicate that complex 1 is more biologically effective than complex 2. In complex 2, however, the high-spin Fe(II) played a key role in regulating its in vitro antitumor effects and seems to be associated with ROS-mediated apoptosis. These findings offer a new avenue for developing anti-cancer drugs by designing complexes with different spin states. [ABSTRACT FROM AUTHOR]

α7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding. However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model. Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals. The α7 nicotinic acetylcholine receptor plays important roles in the human nervous system. Here, authors use molecular simulations to develop a gating model and reveal cholesterol effects, offering insights applicable to drug design. [ABSTRACT FROM AUTHOR]

Macrophages adopt distinct phenotypes in response to environmental cues, with type-2 cytokine interleukin-4 promoting a tissue-repair homeostatic state (M2IL4). Glucocorticoids (GC), widely used anti-inflammatory therapeutics, reportedly impart a similar phenotype (M2GC), but how such disparate pathways may functionally converge is unknown. We show using integrative functional genomics that M2IL4 and M2GC transcriptomes share a striking overlap mirrored by a shift in chromatin landscape in both common and signal-specific gene subsets. This core homeostatic program is enacted by transcriptional effectors KLF4 and the glucocorticoid receptor, whose genome-wide occupancy and actions are integrated in a stimulus-specific manner by the nuclear receptor cofactor GRIP1. Indeed, many of the M2IL4:M2GC-shared transcriptomic changes were GRIP1-dependent. Consistently, GRIP1 loss attenuated phagocytic activity of both populations in vitro and macrophage tissue-repair properties in the murine colitis model in vivo. These findings provide a mechanistic framework for homeostatic macrophage programming by distinct signals, to better inform anti-inflammatory drug design. IL4 and glucocorticoids elicit a homeostatic phenotype in macrophages. Here the authors show that these disparate stimuli yield converging epigenomic and transcriptomic changes, enacted by transcription factors KLF4 and the glucocorticoid receptor, and integrated by their shared coregulator GRIP1. [ABSTRACT FROM AUTHOR]

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions. Many drug targets remain unknown for small molecules. Here, the authors develop a label-free Proteome Integral Solubility Alteration (PISA) workflow to automate target identification and analysis. Applied to rat organs, the authors identify previously unknown drug targets for common medication. [ABSTRACT FROM AUTHOR]

Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered considerable attention. Traditional optimization methods often struggle with the discrete nature of molecular space, but evolutionary computations have demonstrated their versatility across various optimization problems, regardless of the nature of the objective functions. This paper introduces a novel evolutionary algorithm, the Swarm Intelligence-Based Method for Single-Objective Molecular Optimization. Several experiments were conducted to showcase the efficiency of the proposed method, which identifies near-optimal solutions in a remarkably short time. The results were then compared with those of other state-of-the-art methods in the field. [ABSTRACT FROM AUTHOR]

Biological variability poses significant challenges in the development of effective therapeutics, particularly when it comes to drug solubility and bioavailability. Poor solubility across varying physiological conditions often leads to reduced absorption and inconsistent therapeutic outcomes. This review examines how nanotechnology, especially through the use of nanomaterials and magnetic nanoparticles, offers innovative solutions to enhance drug solubility and bioavailability. This comprehensive review focuses on recent advancements and approaches in nanotechnology. We highlight both the successes and remaining challenges in this field, emphasizing the role of continued innovation. Future research should prioritize developing universal therapeutic solutions, conducting interdisciplinary research, and leveraging personalized nanomedicine to address biological variability. [ABSTRACT FROM AUTHOR]

Sortase A (SrtA) of Staphylococcus aureus has long been shown to be a relevant molecular target for antibacterial development. Moreover, the designed SrtA inhibitors act via the antivirulence mechanism, potentially causing less evolutional pressure and reduced antimicrobial resistance. However, no marketed drugs or even drug candidates have been reported until recently, despite numerous efforts in the field. SrtA has been shown to be a tough target for rational structure-based drug design (SBDD), which hampers the regular development of small-molecule inhibitors using the available arsenal of drug discovery tools. Recently, several oligopeptides resembling the sorting sequence LPxTG (Leu-Pro-Any-Thr-Gly) of the native substrates of SrtA were reported to be active in the micromolar range. Despite the good experimental design of those works, their molecular modeling parts are still not convincing enough to be used as a basis for a rational modification of peptidic inhibitors. In this work, we propose to use the ensemble docking approach, in which the relevant SrtA conformations are extracted from the molecular dynamics simulation of the LPRDA (Leu-Pro-Arg-Asp-Ala)-SrtA complex, to effectively represent the most significant and diverse target conformations. The developed protocol is shown to describe the known experimental data well and then is applied to a series of new peptidomimetic molecules resembling the active oligopeptide structures reported previously in order to prioritize structures from this work for further synthesis and activity testing. The proposed approach is compared to existing alternatives, and further directions for its development are outlined. [ABSTRACT FROM AUTHOR]

We describe the design, synthesis and structure–activity relationship of a novel series of 1-(4-(7-azaindole)-3,6-dihydropyridin-1-yl)alkyl-3-(1H-indol-3-yl)pyrrolidine-2,5-dione derivatives with combined effects on the serotonin (5-HT1A) and dopamine (D2) receptors and the serotonin (5-HT), noradrenaline (NA), and dopamine (DA) transporters as multi-target directed ligands for the treatment of depression. All of the tested compounds demonstrated good affinity for the serotonin transporter (SERT). Among them, compounds 11 and 4 emerged as the lead candidates because of their promising pharmacological profile based on in vitro studies. Compound 11 displayed a high affinity for the 5-HT1A (Ki = 128.0 nM) and D2 (Ki = 51.0 nM) receptors, and the SERT (Ki = 9.2 nM) and DAT (Ki = 288.0 nM) transporters, whereas compound 4 exhibited the most desirable binding profile to SERT/NET/DAT among the series: Ki = 47.0 nM/167.0 nM/43% inhibition at 1 µM. These results suggest that compounds 4 and 11 represent templates for the future development of multi-target antidepressant drugs. [ABSTRACT FROM AUTHOR]

The search and development of effective sirtuin small molecule inhibitors (SIRTIs) continues to draw great attention due to their wide range of pharmacological applications. Based on SIRTs' involvement in different biological pathways, their ligands were investigated for many diseases, such as cancer, neurodegenerative disorders, diabetes, cardiovascular diseases and autoimmune diseases. The elucidation of a substantial number of SIRT2–ligand complexes is steering the identification of novel and more selective modulators. Among them, SIRT2 in the presence of the SirReal2 analog series was the most studied. On this basis, we recently reported structure-based analyses leading to the discovery of thiazole-based compounds acting as SIRT2 inhibitors (T1, SIRT2 IC50 = 17.3 µM). Herein, ligand-based approaches followed by molecular docking simulations allowed us to evaluate in silico a novel small series of thiazoles (3a–3d and 5a, 5d) as putative SIRT2 inhibitors. Results from the computational studies revealed comparable molecular interaction fields (MIFs) and docking positionings of most of these compounds with respect to reference SIRT2Is. Biochemical and biological assays validated this study and pointed to compound 5a (SIRT2 IC50 = 9.0 µM) as the most interesting SIRT2I that was worthy of further development as an anticancer agent. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show "GoToCloud", a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD. The design philosophy, implementation, optimization, and performance evaluation of a cloud-computing platform for data analysis and management in Cryo-EM single particle analysis to achieve a significant reduction in processing time and cost. [ABSTRACT FROM AUTHOR]

Although combination therapy is known for its high efficacy, reduced side effects and drug resistance, toxicity remains a major drawback. Some of the genes are likely to induce hepatotoxicity through ROS-mediated mechanisms when a drug is metabolized alone or in combination in the liver. To address this, we have developed a scientific approach to predict the toxicity of different genotoxin combinations and validate their interactions with various targets. The current study is an extensive study of our previous set of in vivo rat liver microarray data processed using R studio for their functional analysis. About five combinations of genotoxins such as CPT/ETP, CPT/CPL, ETP/CPL, CP/CPT and EES/CP along with their differential gene expression targeting Chemical carcinogenesis—ROS are chosen for this study. We aim to examine the binding affinity of different genotoxin combinations using in silico multiple ligand simultaneous docking (MLSD) and are then bio-evaluated for cytotoxicity in vitro using human hepatocellular carcinoma cell lines (HepG2) with the MTT assay. As a result, dose–response cytotoxicity with its strength of interactions and a significant variance in ROS levels in the treated cells is observed compared to their IC50 values. Out of 5 combinations such as CPT/CPL, ETP/CPL and EES/CP are found not only to be significantly cytotoxic but also induce oxidative stress specifically above their IC50 values with good and moderate binding interactions ensuring their toxicity. On the contrary, the safe combinations are found to be CTP/ETP and CP/CPT possibly with no and tolerable adverse effects standing as preliminary information for researchers in drug design and development. [ABSTRACT FROM AUTHOR]

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1β, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug–target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD. [ABSTRACT FROM AUTHOR]

The Sam (Sterile alpha motif) domain of the lipid phosphatase Ship2 (Ship2-Sam) is engaged by the Sam domain of the receptor tyrosine kinase EphA2 (EphA2-Sam) and, this interaction is principally linked to procancer effects. Peptides able to hinder the formation of the EphA2-Sam/Ship2-Sam complex could possess therapeutic potential. Herein, by employing the FoldX software suite, we set up an in silico approach to improve the peptide targeting of the so-called Mid Loop interface of Ship2-Sam, representing the EphA2-Sam binding site. Starting from a formerly identified peptide antagonist of the EphA2-Sam/Ship2-Sam association, first, the most stabilizing mutations that could be inserted in each peptide position were predicted. Then, they were combined, producing a list of potentially enhanced Ship2-Sam ligands. A few of the in silico generated peptides were experimentally evaluated. Interaction assays with Ship2-Sam were performed using NMR and BLI (BioLayer Interferometry). In vitro assays were conducted as well to check for cytotoxic effects against both cancerous and healthy cells, and also to assess the capacity to regulate EphA2 degradation. This study undoubtedly enlarges our knowledge on how to properly target EphA2-Sam/Ship2-Sam associations with peptide-based tools and provides a promising strategy that can be used to target any protein–protein interaction. [ABSTRACT FROM AUTHOR]

Quantum chemical (QC) property prediction is crucial for computational materials and drug design, but relies on expensive electronic structure calculations like density functional theory (DFT). Recent deep learning methods accelerate this process using 1D SMILES or 2D graphs as inputs but struggle to achieve high accuracy as most QC properties depend on refined 3D molecular equilibrium conformations. We introduce Uni-Mol+, a deep learning approach that leverages 3D conformations for accurate QC property prediction. Uni-Mol+ first generates a raw 3D conformation using RDKit then iteratively refines it towards DFT equilibrium conformation using neural networks, which is finally used to predict the QC properties. To effectively learn this conformation update process, we introduce a two-track Transformer model backbone and a novel training approach. Our benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. Quantum chemical (QC) property prediction is crucial in computational chemistry. Here, the authors introduce Uni-Mol+, a deep model that uses iterative updates of 3D molecular conformations to improves the accuracy of QC property prediction. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) is recognized as the gold standard method in fragment‐based drug design for screening, hit validation, and affinity determination. However, its deployment at a large scale is limited by the cost and expertise needed to implement NMR as a routine drug discovery method. The increase in the adoption of fragment‐based drug design created a need for biophysics to provide high‐quality data for weak ligand‐target interactions that can be implemented at scale. NMR must adapt its position in drug design operations to enter this new era. The recent development of commercially available benchtop NMR spectrometers in combination with hyperpolarization methods represents an opportunity for highly deployable and scalable systems. [ABSTRACT FROM AUTHOR]

Bacterial surface display in combination with fluorescence‐activated cell sorting is a versatile and robust system and an interesting alternative approach to phage display for the generation of therapeutic affinity proteins. The system enables real‐time monitoring and sorting of cell populations, which presents unique possibilities for drug development. It has been used to develop several affibody molecules currently being evaluated preclinically for the treatment and diagnosis of, for example, cancer and neurodegenerative diseases. Additionally, it can be implemented in other areas of drug design, such as for mapping epitopes and evolving enzyme specificities. [ABSTRACT FROM AUTHOR]

Gprotein-coupled receptors (GPCRs) regulate several physiological and pathological processes and represent the target of approximately 30% of Food and Drug Administration-approved drugs. GPCR-mediated signaling was thought to occur exclusively at the plasma membrane. However, recent studies have unveiled their presence and function at subcellular membrane compartments. There is a growing interest in studying compartmentalized signaling of GPCRs. This requires development of tools to separate GPCR signaling at the plasma membrane from the ones initiated at intracellular compartments. We leveraged the structural and pharmacological information available for β-adrenergic receptors (βARs) and focused on β1AR as exemplary GPCR that functions at subcellular compartments, and rationally designed spatially restricted antagonists. We generated a cell-impermeable βAR antagonist by conjugating a suitable pharmacophore to a sulfonate-containing fluorophore. This cell-impermeable antagonist only inhibited β1AR on the plasma membrane. In contrast, a cell-permeable βAR antagonist containing a nonsulfonated fluorophore efficiently inhibited both the plasma membrane and Golgi pools of β1ARs. Furthermore, the cell-impermeable antagonist selectively inhibited the phosphorylation of PKA downstream effectors near the plasma membrane, which regulate sarcoplasmic reticulum (SR) Ca2+ release in adult cardiomyocytes, while the β1AR Golgi pool remained active. Our tools offer promising avenues for investigating compartmentalized βAR signaling in various contexts, potentially advancing our understanding of βAR-mediated cellular responses in health and disease. They also offer a general strategy to study compartmentalized signaling for other GPCRs in various biological systems. [ABSTRACT FROM AUTHOR]

Caffeic acid (CA) is a polyphenolic acid compound widely distributed in plant seeds. As natural compounds with high research interest, caffeic acid and its derivatives show good activity in the treatment of tumors and inflammation and have antibacterial properties. In recent years, caffeic acid derivatives have been studied extensively, and these derivatives fall roughly into three categories: (1) caffeic acid ester derivatives, (2) caffeic acid amide derivatives, (3) caffeic acid hybrids. These caffeic acid analogues exert mainly antibacterial and antioxidant activities. Among the caffeic acid analogues summarized in this paper, compounds 1g and CAP10 have good activity against Candida albicans, and their MIC50 is 32 µg/mL and 13 μM, respectively. In a DPPH assay, compounds 3k, 5a, CS2, Phellinsin A and 8j showed strong antioxidant activity, and their IC50 values are 18.6 μM, 67.85 μM, 40.29 μM, 0.29 ± 0.004 mM, 4774.37 ± 137.20 μM, respectively. Overall, compound CAP10 had the best antibacterial activity and compound 3k had the best antioxidant activity. This paper mainly summarizes and discusses some representative caffeic acid analogs, hoping to provide better drug design strategies for the subsequent development of caffeic acid analogs. [ABSTRACT FROM AUTHOR]

Tertiary phosphines have been recognized as powerful organocatalysts for the Michael reaction, which is one of the most important atom-economical methods for carbon-carbon and carbon-heteroatom bond construction. In the presence of tertiary phosphines, a vast array of Michael acceptors and Michael donors can be coupled with each other under neutral and metal-free conditions to produce useful densely functionalized molecules. This review highlights the role of phosphine-catalyzed Michael reaction in cutting-edge areas, such as asymmetric synthesis, natural products synthesis, drug design, and polymer material science. As the central part of catalysis science, the kinetic and mechanistic issues are also discussed when possible. The review is organized by the type of the Michael acceptor and the Michael donor used in the reaction. [ABSTRACT FROM AUTHOR]

The development of all kinds of covalent drugs had a major impact on the improvement of the human health system. Covalent binding to target proteins is achieved by so‐called electrophilic warheads, which are incorporated in the respective drug molecule. In the last decade, specifically acrylamides emerged as attractive warheads in covalent drug design. Herein, a straightforward palladium‐catalyzed hydroaminocarbonylation of acetylene has been developed, allowing a modular and diverse synthesis of bio‐active acrylamides. This general protocol features high atom efficiency, wide functional group compatibility, high chemoselectivity and proceeds additive free under mild reaction conditions. The synthetic utility of this protocol is showcased in the synthesis of ibrutinib, osimertinib, and other bio‐active compound derivatives. [ABSTRACT FROM AUTHOR]

Bridged cyclobutanes and sulfur heterocycles are currently under intense investigation as building blocks for pharmaceutical drug design. Two formal cycloaddition modes involving bicyclobutanes (BCBs) and pyridinium 1,4‐zwitterionic thiolate derivatives were described to rapidly expand the chemical space of sulfur‐containing bridged cyclobutanes. By using Ni(ClO4)2 as the catalyst, an uncommon higher‐order (5+3) cycloaddition of BCBs with quinolinium 1,4‐zwitterionic thiolate was achieved with broad substrate scope under mild reaction conditions. Furthermore, the first Lewis acid‐catalyzed asymmetric polar (5+3) cycloaddition of BCB with pyridazinium 1,4‐zwitterionic thiolate was accomplished. In contrast, pyridinium 1,4‐zwitterionic thiolates undergo an Sc(OTf)3‐catalyzed formal (3+3) reaction with BCBs to generate thia‐norpinene products, which represent the initial instance of synthesizing 2‐thiabicyclo[3.1.1]heptanes (thia‐BCHeps) from BCBs. Moreover, we have successfully used this (3+3) protocol to rapidly prepare thia‐BCHeps‐substituted analogues of the bioactive molecule Pitofenone. Density functional theory (DFT) computations imply that kinetic factors govern the (5+3) cycloaddition reaction between BCB and quinolinium 1,4‐zwitterionic thiolate, whereas the (3+3) reaction involving pyridinium 1,4‐zwitterionic thiolates is under thermodynamic control. [ABSTRACT FROM AUTHOR]

Aims: To summarize the results of clinical studies of insulin icodec, an investigational insulin analog designed for once‐weekly administration, in adults with type 1 and type 2 diabetes. Methods: Thirteen published articles describing clinical studies of insulin icodec were identified in PubMed, and data pertinent to key study outcomes were selected for inclusion in this review. Results: In insulin‐naïve and insulin‐treated individuals, icodec demonstrated efficacy in glycaemic control superior or noninferior to that of insulins glargine U100, glargine U300 and degludec. Icodec exhibited a safety profile comparable to marketed insulins, with the exception of hypoglycaemic event rates. Conclusions: As a once‐weekly alternative to daily basal insulin, icodec is expected to improve patient adherence and satisfaction, reducing the required number of injections per year from 365 to 52 and providing a dosing option potentially attractive to a wide range of insulin users. However, clinical data suggest a notable risk of hypoglycaemia with weekly icodec administration, especially in individuals with type 1 diabetes. [ABSTRACT FROM AUTHOR]

We propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f(drug) → activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F(drug1, drug2) → difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines. We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation. [ABSTRACT FROM AUTHOR]

Metabolism‐related diseases, including diabetes mellitus, obesity, hyperlipidemia, and nonalcoholic fatty liver disease, are becoming increasingly prevalent, thereby posing significant threats to human health and longevity. Proteins, as the primary mediators of biological activities, undergo various posttranslational modifications (PTMs), including phosphorylation, ubiquitination, acetylation, methylation, and SUMOylation, among others, which substantially diversify their functions. These modifications are crucial in the physiological and pathological processes associated with metabolic disorders. Despite advancements in the field, there remains a deficiency in contemporary summaries addressing how these modifications influence processes of metabolic disease. This review aims to systematically elucidate the mechanisms through which PTM of proteins impact the progression of metabolic diseases, including diabetes, obesity, hyperlipidemia, and nonalcoholic fatty liver disease. Additionally, the limitations of the current body of research are critically assessed. Leveraging PTMs of proteins provides novel insights and therapeutic targets for the prevention and treatment of metabolic disorders. Numerous drugs designed to target these modifications are currently in preclinical or clinical trials. This review also provides a comprehensive summary. By elucidating the intricate interplay between PTMs and metabolic pathways, this study advances understanding of the molecular mechanisms underlying metabolic dysfunction, thereby facilitating the development of more precise and effective disease management strategies. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are fundamental to many critical biological processes and are crucial in mediating essential cellular functions across diverse organisms, including bacteria, parasites, and viruses. A notable example is the interaction between the SARS-CoV-2 spike (S) protein and the human angiotensin-converting enzyme 2 (hACE2), which initiates a series of events leading to viral replication. Interrupting this interaction offers a promising strategy for blocking or significantly reducing infection, highlighting its potential as a target for anti-SARS-CoV-2 therapies. This review focuses on the hACE2 and SARS-CoV-2 spike protein interaction, exemplifying the latest advancements in peptide-based strategies for developing PPI inhibitors. We discuss various approaches for creating peptide-based inhibitors that target this critical interaction, aiming to provide potential treatments for COVID-19. [ABSTRACT FROM AUTHOR]