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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]

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle–mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license. [ABSTRACT FROM AUTHOR]

Datafication is driving organizations to invest in data commons, not only to share the costs of data generation, analysis, and curation but, more importantly, to realize synergies in precompetitive research collaborations where private and public motives interact (i.e., semicommons). The fanfare surrounding datafication often hails the sophisticated algorithms used to develop large quantities of data toward greater insight, naively assuming that more data equals better data. Yet for datafication in general and precompetitive research specifically, less attention is awarded to what actually constitutes data and evidence in the first place—that is, to its genesis, construction, and interpretation by heterogeneous scientific and commercial entities. We present the case of Open Targets, a precompetitive collaboration in the life sciences, where publicly funded research, nonprofit foundations, and for-profit pharma collaborate to generate and share data in genomics, proteomics, and bioinformatics. We theorize about the process of data commoning, a political activity in the semicommons where data are created, evidential value is assembled, and scientific meaning converges as data travels, or journeys, across creators, validators, and users. Our findings highlight the effects of relational dynamics and the political nature of data journeys: why these dynamics form, how they are manifested in a precompetitive semicommons, and what implications this can have for the mobility of data as a shared, public good. [ABSTRACT FROM AUTHOR]

α-Glucosidase (α-Glu) is implicated in the progression and pathogenesis of type II diabetes (T2D). In this study, we developed a rapid colorimetric technique using platinum nanoparticles stabilized by chitosan (Ch-PtNPs) to detect α-Glu activity and its inhibitor. The Ch-PtNPs facilitate the conversion of 3,3′,5,5′-tetramethylbenzidine (TMB) into oxidized TMB (oxTMB) in the presence of dissolved O2. The catalytic hydrolysis of 2-O-α-d-glucopyranosyl-l-ascorbic acid (AA-2G) by α-Glu produces ascorbic acid (AA), which reduces oxTMB to TMB, leading to the fading of the blue color. However, the presence of α-Glu inhibitors (AGIs) hinders the generation of AA, allowing Ch-PtNPs to re-oxidize colorless TMB back to blue oxTMB. This unique phenomenon enables the colorimetric detection of α-Glu activity and AGIs. The linear range for α-Glu was found to be 0.1–1.0 U mL−1 and the detection limit was 0.026 U mL−1. Additionally, the half-maximal inhibition value (IC50) for acarbose, an α-Glu inhibitor, was calculated to be 0.4769 mM. Excitingly, this sensing platform successfully detected α-Glu activity in human serum samples and effectively screened AGIs. These promising findings highlight the potential application of the proposed strategy in clinical diabetes diagnosis and drug discovery. [ABSTRACT FROM AUTHOR]

Coupling reactions play a crucial role in drug development enabling the rapid expansion of structure–activity relationships (SARs) during drug discovery programs to identify a clinical candidate and simplify subsequent drug development processes. In particular, their relevance in clinical medicine and drug discovery has increased significantly in the last two decades. To facilitate these metal-catalyzed coupling reactions, suitably designed ligands are necessary and from the industrial point of view, sustainable and cost-effective ligands are of current need. Benzotriazole, a non-toxic, thermally stable, and inexpensive bidentate ligand, exhibits strong electron donating and electron accepting properties, along with excellent solubility in various organic solvents. It has been extensively explored as a synthetic auxiliary in the past; in recent years, benzotriazole and its derivatives have been used as ligands in metal-catalyzed coupling reactions. The facile construction of carbon–carbon and carbon–heteroatom bonds in the presence of versatile benzotriazole ligands makes it an indispensable ligand for catalytic transformations. The present feature article mainly emphasizes the advances in the utilization of benzotriazole as a ligand in a diverse range of C–C, C–N, C–O, and C–S coupling reactions. [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]

Substance use disorder (SUD) is a chronic condition, with maintained abuse of a substance leading to physiological and psychological alterations and often changes in cognitive and social behaviours. Current therapies include psychotherapy coupled with medication; however, high relapse rates reveal the shortcomings of these therapies. The signalling, expression profile, and neurological function of the serotonin 2C receptor (5‐HT2C receptor) make it a candidate of interest for the treatment of SUD. Recently, psychedelics, which broadly act at 5‐HT2 receptors, have indicated potential for the treatment of SUD, implicating the 5‐HT2C receptor. The modern psychedelic movement has rekindled interest in the 5‐HT2C receptor, resulting in many new studies, especially structural analyses. This review explores the structural, molecular and cellular mechanisms governing 5‐HT2C receptor function in the context of SUD. This provides the basis of the preclinical and clinical evidence for their role in SUD and highlights the potential for future exploration. [ABSTRACT FROM AUTHOR]

In this Perspective, the use of DNA nanotechnology is explored as a powerful tool for studying a family of enzymes known as topoisomerases. These enzymes regulate DNA topology within a living cell and play a major role in the pharmaceutical field, serving as anti‐cancer and anti‐bacterial targets. This Perspective will provide a short historical overview of the methods employed in studying these enzymes and emphasizing recent advancements in assays using DNA nanotechnology. These innovations have substantially improved accuracy and expanded the understanding of enzyme activity. This perspective will showcase the versatile utility of DNA nanotechnology in advancing scientific knowledge and its application in exploring new drug candidates, particularly in the study of topoisomerase enzymes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Abnormal activation of Group I mGluRs induces central sensitization. • Blocking Group Ⅰ mGluRs can alleviate neuropathic pain. • The existing Group I mGluRs orthosteric site antagonists have limitations. • Allosteric Modulators exhibit natural advantages due to allosteric modulation. • Structure based drug discovery may promote drug discovery targeting Group I mGluRs. Neuropathic pain (NP) is a widespread public health problem that existing therapeutic treatments cannot manage adequately; therefore, novel treatment strategies are urgently required. G-protein-coupled receptors are important for intracellular signal transduction, and widely participate in physiological and pathological processes, including pain perception. Group I metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, are predominantly implicated in central sensitization, which can lead to hyperalgesia and allodynia. Many orthosteric site antagonists targeting Group I mGluRs have been found to alleviate NP, but their poor efficacy, low selectivity, and numerous side effects limit their development in NP treatment. Here we reviewed the advantages of Group I mGluRs negative allosteric modulators (NAMs) over orthosteric site antagonists based on allosteric modulation mechanism, and the challenges and opportunities of Group I mGluRs NAMs in NP treatment. This article aims to elucidate the advantages and future development potential of Group I mGluRs NAMs in the treatment of NP. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the concentration of a drug and its therapeutic efficacy or side effects is crucial in drug development, especially to understand therapeutic efficacy in central nervous system drug, quantifying drug-induced site-specific changes in the levels of endogenous metabolites, such as neurotransmitters. In recent times, evaluation of quantitative distribution of drugs and endogenous metabolites using matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) has attracted much attention in drug discovery research. However, MALDI-MSI quantification (quantitative mass spectrometry imaging, QMSI) is an emerging technique, and needs to be further developed for practicable and convenient use in drug discovery research. In this study, we developed a reliable QMSI method for quantification of clozapine (antipsychotic drug) and dopamine and its metabolites in the rat brain using MALDI-MSI. An improved mimetic tissue model using powdered frozen tissue for QMSI was established as an alternative method, enabling the accurate quantification of clozapine levels in the rat brain. Furthermore, we used the improved method to evaluate drug-induced fluctuations in the concentrations of dopamine and its metabolites. This method can quantitatively evaluate drug localization in the brain and drug-induced changes in the concentration of endogenous metabolites, demonstrating the usefulness of QMSI. [ABSTRACT FROM AUTHOR]

Solid cancers frequently relapse with distant metastasis, despite local and systemic treatment. Cellular dormancy has been identified as an important mechanism underlying drug resistance enabling late relapse. Therefore, relapse from invisible, minimal residual cancer of seemingly disease-free patients call for in vitro models of dormant cells suited for drug discovery. Here, we explore dormancy-inducing 3D engineered matrices, which generate mechanical confinement and induce growth arrest and survival against chemotherapy in cancer cells. We characterized the dormant phenotype of solitary cells by P-ERKlow: P-p38high dormancy signaling ratio, along with Ki67-expression. As underlying mechanism, we identified stiffness-dependent nuclear localization of the four-and-a-half LIM domain 2 (FHL2) protein, leading to p53-independent high p21Cip1/Waf1 nuclear expression, validated in murine and human tissue. Suggestive of a resistance-causing role, cells in the dormancy-inducing matrix became sensitive against chemotherapy upon FHL2 down-regulation. Thus, our biomaterial-based approach will enable systematic screens for previously unidentified compounds suited to eradicate potentially relapsing dormant cancer cells. [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]

Background Study Design and Methods Results Conclusion The characteristic feature of immune cytopenias involves the process of extravascular phagocytosis, wherein macrophages in the spleen and/or liver engage in the destruction of blood cells that have been opsonized by auto‐ or alloantibodies. Therefore, new treatments that prevent phagocytosis will be advantageous, especially for short‐term usage along with alternative options.KB‐208, a small molecule drug, previously shown to be efficacious for the in vitro inhibition of phagocytosis was synthesized. A passive antibody mouse model of immune thrombocytopenia (ITP) was used. Three different mouse strains (BALB/c, C57BL/6, CD1) were used to determine the efficacy of KB‐208 compared with IVIG to ameliorate the ITP. Toxicity was investigated after 60‐day chronic administration of KB‐208 by a biochemistry panel, gross necroscopy and histopathology.KB‐208 showed similar efficacy to ameliorate the thrombocytopenia compared with IVIG in all three mouse strains. This small molecule drug was effective at 1 mg/kg in ameliorating ITP, in comparison with IVIG at 1000–2500 mg/kg. KB‐208 did not affect other blood parameters or elevate serum biochemistry markers of toxicity nor were any abnormal histopathological findings found.KB‐208 is similar to IVIG for the amelioration of ITP in multiple mouse strains. Chronic administration of KB‐208 for 60 days did not demonstrate in vivo toxicity. These findings indicate that KB‐208 is efficacious, without significant in vivo toxicities in mice, and is a potential small molecule candidate for further evaluation to be used in the treatment of ITP and possibly all immune cytopenias where phagocytosis is responsible for the pathophysiology. [ABSTRACT FROM AUTHOR]

The "New Journal of Chemistry" published by the Royal Society of Chemistry features a variety of research articles in the field of chemistry. The latest issue includes papers on topics such as the synthesis of acyclic analogues of adenosine sulfonamides, construction of covalent organic frameworks, and studies on various chemical compounds and reactions. The journal aims to connect the global chemistry community and invest profits back into the field of chemistry. [Extracted from the article]

Thiophene containing drugs have been developed and show up among many important drugs currently in the market, like Plavix®, Cymbalta® and Tomudex®. However, this important organic group is still not present in the fluorine fragment libraries found in the literature. To fix this and contribute to increasing the structural diversity of fragment libraries, we synthesized a fluorinated, bicyclic, thiophene-based fragment library in a modular fashion to be screened via ligand observed 19F NMR against drug targets. For certain compounds in the synthesized library, binding was detected against the HRAS mutant G12V, a notoriously undruggable cancer target, and further confirmed with 15N HSQC NMR experiments. To exclude the possibility that the binding was promiscuous, the best binding fragment was screened against an unrelated protein, RNase 5 and was shown to be a non-binder. [ABSTRACT FROM AUTHOR]

The efficient identification and validation of drug targets are paramount in drug discovery and development. Excessive costs, intricate procedures, and laborious sample handling frequently encumber contemporary methodologies. In this study, we introduce an innovative approach for the expeditious screening of drug targets utilizing solid-state nanopores. These nanopores provide a label-free, ultra-sensitive, and high-resolution platform for the real-time detection of biomolecular interactions. By observing the changes in relative ion currents over time after mixing different peptides with small molecule drugs, and supplementing this with noise analysis, we can pinpoint specific regions of drug action, thereby enhancing both the speed and cost-efficiency of drug development. This research offers novel insights into drug discovery, expands current perspectives, and lays the groundwork for formulating effective therapeutic strategies across a spectrum of diseases. [ABSTRACT FROM AUTHOR]

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFβ-stimulated control (1 μM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification. [ABSTRACT FROM AUTHOR]

Thiophene containing drugs have been developed and show up among many important drugs currently in the market, like Plavix®, Cymbalta® and Tomudex®. However, this important organic group is still not present in the fluorine fragment libraries found in the literature. To fix this and contribute to increasing the structural diversity of fragment libraries, we synthesized a fluorinated, bicyclic, thiophene-based fragment library in a modular fashion to be screened via ligand observed 19F NMR against drug targets. For certain compounds in the synthesized library, binding was detected against the HRAS mutant G12V, a notoriously undruggable cancer target, and further confirmed with 15N HSQC NMR experiments. To exclude the possibility that the binding was promiscuous, the best binding fragment was screened against an unrelated protein, RNase 5 and was shown to be a non-binder. [ABSTRACT FROM AUTHOR]

Conventional cell culture techniques generally fail to recapitulate the expression profiles or functional phenotypes of the in vivo equivalents they are meant to model. These cell culture models are indispensable for preclinical drug discovery and mechanistic studies. However, if our goal is to develop effective therapies that work as intended in the human body, we must revise our cell culture models to recapitulate normal and disease physiology to ensure that we identify compounds that are useful and effective beyond our in vitro models. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus is a metabolic disease categorized by hyperglycemia, resistance to insulin, and ß-cell dysfunction. Around the globe, approximately 422 million people have diabetes, out of which 1.5 million die annually. In spite of innovative advancements in the treatment of diabetes, no biological drug has been known to successfully cure and avert its progression. Thereupon, natural drugs derived from plants are emerging as a novel therapeutic strategy to combat diseases like diabetes. Objective: The current study aims to investigate the antidiabetic potential of natural compounds of Oryza sativa L. indica (black rice) in disease treatment. Methods: Antioxidant activity and alpha amylase assays were performed to evaluate the therapeutic potential of the extract of Oryza sativa L. indica. Gas chromatography–mass spectrometry (GC–MS) was used for identification of constituents from the ethanol extract. ADMET profiling (absorption, distribution, metabolism, excretion, and toxicity), network pharmacology, and molecular dynamics simulation were employed in order to uncover the active ingredients and their therapeutic targets in O. sativa L. indica against type 2 diabetes mellitus. Results: GC–MS of the plant extract provided a list of 184 compounds. Lipinski filter and toxicity parameters screened out 18 compounds. The topological parameters of the protein–protein interaction (PPI) were used to shortlist the nine key proteins (STAT3, HSP90AA1, AKT1, SRC, ESR1, MAPK1, NFKB1, EP300, and CREBBP) in the type 2 diabetes mellitus pathways. Later, molecular docking analysis and simulations showed that C14 (1H-purine-8-propanoic acid,.alpha.-amino-2, 3, 6, 7-tetrahydro-1,3,7-trimethyl-2,6-dioxo-) and C18 (cyclohexane-carboxamide, N-furfuryl) bind with AKT1 and ESR1 with a binding energy of 8.1, 6.9, 7.3, and 7.2 kcal/mol, respectively. RMSD (root-mean-square deviation) and RMSF (root-mean-square fluctuation) values for AKT1 and ESR1 have shown very little fluctuation, indicating that proteins were stabilized after ligand docking. Conclusion: This study suggests therapeutic drug candidates against AKT1 and ESR1 to treat type 2 diabetes mellitus. However, further wet-lab analysis is required to discover the best remedy for type 2 diabetes mellitus. [ABSTRACT FROM AUTHOR]

Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug–target interaction pairs. In this study, we propose a novel drug–target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug–target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations. [ABSTRACT FROM AUTHOR]

Triterpenes are structurally complex natural products with promising therapeutic properties. Recalcitrance to chemical synthesis has hindered their use in drug development. Recent advances now make it possible to access and harness triterpene structural diversity using engineering biology approaches, enabling the discovery and optimisation of a new generation of drug leads. [ABSTRACT FROM AUTHOR]

Reversible, non-covalent small molecule protein interactions are central to our understanding of biology and the development of therapeutics. Globally assessing all binding sites of a particular small molecule in a given system can be critical to defining mechanisms of action and assessing off-target effects. Emerging techniques to quantitatively map small molecule binding sites, largely driven by photoaffinity labeling (PAL) approaches, are expanding our knowledge of the ligandable proteome. Integrating small molecule binding site mapping with other technologies [e.g., structural modeling, artificial intelligence/machine learning (AI/ML)] will continue to improve our understanding of small molecule–protein interactions. The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule–protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this opinion article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development. The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule–protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this review article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development. [ABSTRACT FROM AUTHOR]

The development of new drugs is a vital effort that has the potential to improve human health, well-being and life expectancy. Molecular property prediction is a crucial step in drug discovery, as it helps to identify potential therapeutic compounds. However, experimental methods for drug development can often be time-consuming and resource-intensive, with a low probability of success. To address such limitations, deep learning (DL) methods have emerged as a viable alternative due to their ability to identify high-discriminating patterns in molecular data. In particular, graph neural networks (GNNs) operate on graph-structured data to identify promising drug candidates with desirable molecular properties. These methods represent molecules as a set of node (atoms) and edge (chemical bonds) features to aggregate local information for molecular graph representation learning. Despite the availability of several GNN frameworks, each approach has its own shortcomings. Although, some GNNs may excel in certain tasks, they may not perform as well in others. In this work, we propose a hybrid approach that incorporates different graph-based methods to combine their strengths and mitigate their limitations to accurately predict molecular properties. The proposed approach consists in a multi-layered hybrid GNN architecture that integrates multiple GNN frameworks to compute graph embeddings for molecular property prediction. Furthermore, we conduct extensive experiments on multiple benchmark datasets to demonstrate that our hybrid approach significantly outperforms the state-of-the-art graph-based models. The data and code scripts to reproduce the results are available in the repository, https://github.com/pedro-quesado/HybridGNN. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein–protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type–specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. Key messages: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD. [ABSTRACT FROM AUTHOR]

The reproducibility of chemical reactions, when obtaining protocols from literature or databases, is highly challenging for academicians, industry professionals and even now for the machine learning process. To synthesize the organic molecule under the photochemical condition, several years for the reaction optimization, highly skilled manpower, long reaction time etc. are needed, resulting in non-affordability and slow down the research and development. Herein, we have introduced the DigiChemTree backed with the artificial intelligence to auto-optimize the photochemical reaction parameter and synthesizing the on demand library of the molecules in fast manner. Newly, auto-generated digital code was further tested for the late stage functionalization of the various active pharmaceutical ingredient. Light-induced reactions of diazo compounds have become crucial in organic synthesis and drug discovery, however, optimization of reaction conditions is still very time-consuming. Here, the authors develop a DigiChemTree platform using artificial intelligence to auto-optimize the photochemical reaction parameters and rapidly synthesize an on-demand library of molecules. [ABSTRACT FROM AUTHOR]

Several approaches can be used to genetically modify the human malaria parasite Plasmodium falciparum to express reporter proteins, such as fluorescent proteins and luciferases, and their expression can be controlled using stage-specific or constitutive promoters. The expression of fluorescent proteins in malaria parasites enables molecular analyses, such as live imaging, flow cytometry, and cell sorting. The number of parasites at each stage can be quantified using the luciferase luminescent signal to evaluate the antimalarial effects of the compounds of interest and the inhibitory effects of antibodies. Further parasite reporter lines are needed to unveil the biological aspects of human malaria parasites and boost drug discovery against this deadly pathogen. The human malaria parasite Plasmodium falciparum causes the most severe form of malaria in endemic regions and is transmitted via mosquito bites. To better understand the biology of this deadly pathogen, a variety of P. falciparum reporter lines have been generated using transgenic approaches to express reporter proteins, such as fluorescent proteins and luciferases. This review discusses the advances in recently generated P. falciparum transgenic reporter lines, which will aid in the investigation of parasite physiology and the discovery of novel antimalarial drugs. Future prospects for the generation of new and superior human malaria parasite reporter lines are also discussed, and unresolved questions in malaria biology are highlighted to help boost support for the development and implementation of malaria treatments. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is the most common form of arthritis and a leading cause of pain and disability in adults. A central feature is progressive cartilage degradation and matrix fragment formation driven by the excessive production of matrix metalloproteinases (MMPs), such as MMP-13, by articular chondrocytes. Inflammatory factors, including interleukin 6 (IL-6), are secreted into the joint by synovial fibroblasts, and can contribute to pain and inflammation. No therapeutic exists that addresses the underlying loss of joint tissue in OA. To address this, we developed and utilized a cell-based high-throughput OA drug discovery platform using normal human chondrocytes treated with a recombinant fragment of the matrix protein fibronectin (FN-f) as a catabolic stimulus relevant to OA pathogenesis and a readout using a fluorescent MMP-13 responsive probe. The goal was to test this screening platform by identifying compounds that inhibited FN-f-induced MMP-13 production and determine if these compounds also inhibited catabolic signaling in OA chondrocytes and synovial fibroblasts. Two pilot screens of 1344 small molecules revealed five "hits" that strongly inhibited FN-f induced MMP-13 production with low cytotoxicity. These included RO-3306 (CDK1 inhibitor (i)), staurosporine (PKCi), trametinib (MEK1 and MEK2i), GSK-626616 (DYRK3i), and edicotinib (CSF-1Ri). Secondary testing using immunoblots and cells derived from OA joint tissues confirmed the ability of selected compounds to inhibit chondrocyte MMP-13 production and FN-f stimulated IL-6 production by synovial fibroblasts. These findings support the use of this high throughput screening assay for discovery of disease-modifying osteoarthritis drugs. [ABSTRACT FROM AUTHOR]

Aberrant fucosylation is the hallmark of malignant cell transformation, leading to many cellular events, such as uncontrolled cell proliferation, angiogenesis, tumor cell invasion, and metastasis. This increased fucosylation is caused due to the over-expression of fucosyltransferases (FUTs) that catalyzes the transfer of the fucose (Fuc) residue from GDP-fucose (donor substrate) to various oligosaccharides, glycoproteins, and glycolipids (acceptor substrates). Hence, fucosyltransferases (FUTs) are considered as validated target for the drug discovery against on cancers. In the current study, a drug repurposing approach was deployed to identify new hits against fucosyltransferase 2 (FUT2), using computational and biophysical techniques. A library of 500 US-FDA approved drugs were screened in-silico against fucosyltransferase 2 (FUT2) donor and acceptor sites. Five drugs were predicted as hits, based on their significant docking scores (-5.8 to -8.2), and binding energies (-43 to -51.19 Kcal/mol). Furthermore, STD-NMR highlighted the epitope of these drugs in the binding site of fucosyltransferase 2 (FUT2). Simulation studies provided insights about the binding site of these drugs, and 4 of them, acarbose, ascorbic acid, ibuprofen, and enalaprilat dihydrate, were found as significant binders at the donor binding site of fucosyltransferase 2 (FUT2). Hence, the current study reports the repurposed drugs as potential hits against fucosyltransferase 2 (FUT2). These may be further studied through in-vitro and in-vivo inhibitory and mechanistic studies. [ABSTRACT FROM AUTHOR]

The article discusses the integration of artificial intelligence (AI) in clinical pharmacology, highlighting its potential in drug discovery, personalized medicine, and patient care. AI tools can accelerate drug development, identify new drug-target pairs, and optimize clinical trials. While AI offers significant advancements, challenges such as ethical concerns, data biases, and integration hurdles must be addressed. Collaboration between technologists, clinicians, and ethicists is crucial for harnessing AI's potential while safeguarding against risks in healthcare settings. [Extracted from the article]

Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) unfolds with pathological processes and neurodegeneration well before the emergence of noticeable motor symptoms, providing a window for early identification. The extended prodromal phase allows the use of risk stratification measures and prodromal markers to pinpoint individuals likely to develop PD. Importantly, a growing body of evidence emphasizes the heterogeneity within prodromal and clinically diagnosed PD. The disease likely comprises distinct subtypes exhibiting diverse clinical manifestations, pathophysiological mechanisms, and patterns of α-synuclein progression in the central and peripheral nervous systems. There is a pressing need to refine the definition and early identification of these prodromal subtypes. This requires a comprehensive strategy that integrates genetic, pathological, imaging, and multi-omics markers, alongside careful observation of subtle motor and non-motor symptoms. Such multi-dimensional classification of early PD subtypes will improve our understanding of underlying disease pathophysiology, improve predictions of clinical endpoints, progression trajectory and medication response, contribute to drug discovery and personalized medicine by identifying subtype-specific disease mechanisms, and facilitate drug trials by reducing confounding effects of heterogeneity. Here we explore different subtyping methodologies in prodromal and clinical PD, focusing on clinical, imaging, genetic and molecular subtyping approaches. We also emphasize the need for refined, theoretical a priori disease models. These will be prerequisite to understanding the biological underpinnings of biological subtypes, which have been defined by large scale data-driven approaches and multi-omics fingerprints. Plain Language Summary: Parkinson's disease is an incurable neurodegenerative disorder characterized by a 20-year prodromal phase before an individual is formally diagnosed. During this prodromal or early disease phase, non-motor symptoms gradually accumulate including a reduced ability to smell, sleep disturbance, constipation, depression, anxiety, and memory problems. Subtle motor symptoms including slowness of movement, stiffness, and tremor may be present, but not to the extent required for a clinical diagnosis. Each individual with prodromal Parkinson's disease is affected with a unique combination of these symptoms which progress at different rates. Subtyping attempts to understand this variability by defining groups of patients with sets of key features at a clinical, genetic, imaging, or molecular level. This article reviews subtyping approaches and how they might improve our understanding of how Parkinson's disease evolves. [ABSTRACT FROM AUTHOR]

Breast cancer remains a major health threat throughout the world specifically in women above 30 years of age however, it is rarely known to affect men as well. It is characterized by the abnormal division of cells in the breast tissue resulting in the development of breast malignancies. Various risk factors contributing to breast cancer include age, family history, genetic mutations (chiefly in BRCA1 and BRCA2 genes) along with hormonal imbalances (oestrogen, progesterone, HER2). Early detection which can be obtained through frequent rounds of self-examination, mammographic scanning, and clinical assessment plays a crucial role in the prevention of the disease. In addition, appropriate diagnosis assists in better therapeutic responses. This study highlights the considerable health risks associated with the conventional treatment procedures which arise and increased demand of advanced, secure, and risk-free treatment alternatives. Oncolytic viruses are potentially apparent for the aim of improving cancer therapeutics with reduced side effects. These viruses act as the fundamental therapeutic agent themselves that selectively target and kill malignant cells without harm to healthy tissues. The key objective of the research is to provide evidence that Human Adenovirus 52 is a potent oncolytic virus and to highlight its capacity to target and eliminate cancer cells with precision while causing the least amount of harm to healthy tissues. Validating the in-silico method entails evaluating the precision and dependability of the computational modelling by contrasting the in-silico predictions with the findings from the experiments rank as the secondary objective. The workflow of this research utilized in-silico computational drug designing approaches including retrieval of tertiary structures of both the target Breast Cancer Type 1 Susceptibility Protein (BRCA1) and the viral Human Adenovirus 52 protein, their validation generating Ramachandran Plots determining favoured amino acid residue angles and prediction of their active residues. Furthermore, the study focused on the molecular dynamics docking of proteins, interpretation of molecular interactions between the docked complex, as well as the assessment of the molecular dynamic simulations (MD) in addition to their MMGBSA binding energy calculations. A successful docking between BRCA1 and Adenovirus protein provided a significant score of 329.2 +/- 24.3, furthermore, MD simulations showed a high RMSD peak at 2.8 Å, RMSF were maximum at 3.5 Å with highest protein–protein interaction, the radius of gyration was stable throughout the simulation representing elastic stability along with a high energy interaction value of - 7882 kCal/mol. Moreover, the MMGBSA calculation results showed a notable release of binding free energy of - 68.96 kCal/mol demonstrating effective bond formation between the docked complex. These findings propose the effectiveness of Human Adenovirus 52 to treat cancer. The selected oncolytic Human Adenovirus 52 is a potential candidate for the target specific treatment of breast cancer through virotherapy. This computer-aided drug discovery presents significant potential in targeting cancer cells and would assist in the development of potent drug reagents for the cancer therapy. [ABSTRACT FROM AUTHOR]

SARS CoV-2 virus (COVID-19) emerged as a highly infectious human pathogen in late 2019. Started in China but rapidly started spreading all over the world and soon became a pandemic. More than seven million deaths have been reported so far that devasted millions of families worldwide. Several drugs, such as hydroxychloroquine, chloroquine, remdesivir, favipiravir and arbidol have undergone some clinical studies since early 2020, but their safety concerns remained a serious issue. However, within an year from late 2019, several successful vaccines were invented to prevent people from the infection. Almost at the same time, two drugs were discovered. These are PAXLOVID™ (nirmatrelvir, ritonavir) from Pfizer and molnupiravir from Merck which were soon approved by the US-FDA, for emergency use in the treatment of COVID-19. But several challenges were soon reported in treatments with these drugs, particularly for those who are immunocompromised or have vaccine immunity and suffering for a long time from the infection (known as long-COVID). Complex issues for treatments of long-COVID patients continue to remain unresolved. Thus, discovery of new drugs to assist treatment for emerging COVID-19 problems is urgently needed. But it is important to note that discovery of new COVID-19 therapeutics, particularly small molecules is not a simple task. However, there are several excellent reviews on attempts for COVID-19 drug discovery including a handful of articles on theoretical approaches towards the goal. This review summarizes the theoretical attempts for discovery of COVID-19 drugs, their challenges and future opportunities along with efforts from the author's lab. [ABSTRACT FROM AUTHOR]

Background: Current treatment of major depressive disorder is facing challenges, including a low remission rate, late onset of efficacy, and worsening severity due to comorbid symptoms such as psychosis and cognitive dysfunction. Serotonin (5-HT) neurotransmission is involved in a wide variety of psychiatric diseases and its potential as a drug target continues to attract attention. Objectives: The present study elucidates the effects of a novel 5-HT modulator, DSP-6745, on depression and its comorbid symptoms. Results: In vitro radioligand binding and functional assays showed that DSP-6745 is a potent inhibitor of 5-HT transporter and 5-HT2A, 5-HT2C, and 5-HT7 receptors. In vivo, DSP-6745 (6.4 and 19.1 mg/kg as free base, p.o.) increased the release of not only 5-HT, norepinephrine, and dopamine, but also glutamate in the medial prefrontal cortex. The results of in vivo mouse phenotypic screening by SmartCube® suggested that DSP-6745 has a behavioral signature combined with antidepressant-, anxiolytic-, and antipsychotic-like signals. A single oral dose of DSP-6745 (6.4 and 19.1 mg/kg) showed rapid antidepressant-like efficacy in the rat forced swim test, even at 24 h post-dosing, and anxiolytic activity in the rat social interaction test. Moreover, DSP-6745 (12.7 mg/kg, p.o.) led to an improvement in the apomorphine-induced prepulse inhibition deficit in rats. In the marmoset object retrieval with detour task, which is used to assess cognitive functions such as attention and behavioral inhibition, DSP-6745 (7.8 mg/kg, p.o.) enhanced cognition. Conclusions: These data suggest that DSP-6745 is a multimodal 5-HT receptor antagonist and a 5-HT transporter inhibitor and has the potential to be a rapid acting antidepressant with efficacies in mitigating the comorbid symptoms of depression. [ABSTRACT FROM AUTHOR]

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes. • The confidence method based on causal intervention is applied to drug target link prediction. • Three data sets and nine KGE models were used to verify the robustness of the proposed method. • The robustness of the results was evaluated by the optimal score and the dimension of the intervention input vector. • A comprehensive introduction to the algorithm and extensive experiments are conducted. [ABSTRACT FROM AUTHOR]

Chronic pain is a constantly recurring and persistent illness, presenting a formidable healthcare challenge for patients and physicians alike. Current first‐line analgesics offer only low‐modest efficacy when averaged across populations, further contributing to this debilitating disease burden. Moreover, many recent trials for novel analgesics have not met primary efficacy endpoints, which is particularly striking considering the pharmacological advances have provided a range of highly relevant new drug targets. Heterogeneity within chronic pain cohorts is increasingly understood to play a critical role in these failures of treatment and drug discovery, with some patients deriving substantial benefits from a given intervention while it has little‐to‐no effect on others. As such, current treatment failures may not result from a true lack of efficacy, but rather a failure to target individuals whose pain is driven by mechanisms which it therapeutically modulates. This necessitates a move towards phenotypical stratification of patients to delineate responders and non‐responders in a mechanistically driven manner. In this article, we outline a bench‐to‐bedside roadmap for this transition to mechanistically informed personalised pain medicine. We emphasise how the successful identification of novel analgesics is dependent on rigorous experimental design as well as the validity of models and translatability of outcome measures between the animal model and patients. Subsequently, we discuss general and specific aspects of human trial design to address heterogeneity in patient populations to increase the chance of identifying effective analgesics. Finally, we show how stratification approaches can be brought into clinical routine to the benefit of patients. [ABSTRACT FROM AUTHOR]

Disease-modifying therapies (DMT) for Alzheimer's disease (AD) are highly longed-for. In this quest, anti-amyloid therapies take center stage supported by genetic facts that highlight an imbalance between production and clearance of amyloid-β peptide (Aβ) in AD patients. Indeed, evidence from basic research, human genetic and biomarker studies, suggests the accumulation of Aβ as a driver of AD pathogenesis and progression. The aspartic protease β-site AβPP cleaving enzyme (BACE1) is the initiator for Aβ production. Underpinning a critical role for BACE1 in AD pathophysiology are the elevated BACE1 concentration and activity observed in the brain and body fluids of AD patients. Therefore, BACE1 is a prime drug target for reducing Aβ levels in early AD. Small-molecule BACE1 inhibitors have been extensively developed for the last 20 years. However, clinical trials with these molecules have been discontinued for futility or safety reasons. Most of the observed adverse side effects were due to other aspartic proteases cross-inhibition, including the homologue BACE2, and to mechanism-based toxicity since BACE1 has substrates with important roles for synaptic plasticity and synaptic homeostasis besides amyloid-β protein precursor (AβPP). Despite these setbacks, BACE1 persists as a well-validated therapeutic target for which a specific inhibitor with high substrate selectivity may yet to be found. In this review we provide an overview of the evolution in BACE1 inhibitors design pinpointing the molecules that reached advanced phases of clinical trials and the liabilities that precluded adequate trial effects. Finally, we ponder on the challenges that anti-amyloid therapies must overcome to achieve clinical success. [ABSTRACT FROM AUTHOR]

The failure rate of drugs being developed for neuropsychiatric indications remains high. Optimizing drug discovery and development requires not only a better neurobiological understanding of disease aetiology and development, but also the means by which we can measure relevant biological and clinical processes related to disease progression, drug target engagement, and sensitivity to treatment. Here we address the role and key considerations for the selection of biomarkers in clinical drug development for neuropsychiatric disorders. We do not provide an exhaustive list of biomarkers; rather we lay out a pragmatic, well-defined biomarker selection strategy that addresses the main goals for each of the phases in the drug development cycle. We discuss the key questions and issues that concern biomarker selection and implementation in each phase of development. For the better development of biomarkers, we emphasize the need to focus on discrete biological dysfunction and/or symptom domains rather than diagnoses. We also advocate the use of biomarker-based patient stratification in phase 2 and 3 to increase sensitivity and power and reduce costs. Our aim is to enhance precision and chances of success for these complex and heterogeneous brain disorders with a high unmet medical need. [ABSTRACT FROM AUTHOR]

Cellular protein kinases are involved in diverse normal cellular functions. Many types of dysregulation of protein kinases are the molecular basis for development of common cancers and neurodegenerative diseases. More than 80 small-molecule protein kinase inhibitors are available now and approved by the US Food and Drug Administration for successful treatment of cancers and neurodegenerative diseases. Newly designed protein kinase inhibitors and related forms of therapy based on a greater understanding of molecular mechanisms have diminished the appearance of disease resistance to protein kinase inhibitors and other side effects. These advances will further promote the success of protein kinase inhibitors in treatment of common cancers, Alzheimer's disease, and other neurodegenerative conditions. [ABSTRACT FROM AUTHOR]

Cancer therapeutic resistance as a common hallmark of cancer is often responsible for treatment failure and poor patient survival. Cancer stem-like cells (CSCs) are one of the main contributors to therapeutic resistance, cancer relapse and metastasis. Through screening from our in-house library of natural products, we found polyphyllin II (PPII) as a potent anti-CSC compound for triple-negative breast cancer (TNBC). To enhance anti-CSC selectivity and improve druggability of PPII, we leverage the liposome-mediated delivery technique for increasing solubility of PPII, and more significantly, attaining broader therapeutic window. Liposomal PPII demonstrates its marked potency to inhibit tumor growth, post-surgical recurrence and metastasis compared to commercial liposomal chemotherapeutics in the mouse models of CSC-enriched TNBC tumor. We further identify PPII as an inhibitor of the Ras-related nuclear (RAN) protein whose upregulated expression is correlated with poor clinical outcomes. The direct binding of PPII to RAN reduces TNBC stemness, thereby suppressing tumor progression. Our work offers a significance from drug discovery to drug delivery benefiting from liposome technique for targeted treatment of high-stemness tumor. [Display omitted] • PPII, a natural product, effectively eliminates CSC-like cell population. • Liposome delivery enhances anticancer selectivity and efficacy of PPII. • RAN is identified as novel target of PPII. • PPII binds RAN to reduce tumor stemness phenotype. [ABSTRACT FROM AUTHOR]

The Kirsten Rat Sarcoma (KRAS) G12D mutant protein is a primary driver of pancreatic ductal adenocarcinoma, necessitating the identification of targeted drug molecules. Repurposing of drugs quickly finds new uses, speeding treatment development. This study employs microsecond molecular dynamics simulations to unveil the binding mechanisms of the FDA‐approved MEK inhibitor trametinib with KRASG12D, providing insights for potential drug repurposing. The binding of trametinib was compared with clinical trial drug MRTX1133, which demonstrates exceptional activity against KRASG12D, for better understanding of interaction mechanism of trametinib with KRASG12D. The resulting stable MRTX1133‐KRASG12D complex reduces root mean square deviation (RMSD) values, in Switch I and II domains, highlighting its potential for inhibiting KRASG12D. MRTX1133's robust interaction with Tyr64 and disruption of Tyr96‐Tyr71‐Arg68 network showcase its ability to mitigate the effects of the G12D mutation. In contrast, trametinib employs a distinctive binding mechanism involving P‐loop, Switch I and II residues. Extended simulations to 1 μs reveal sustained network interactions with Tyr32, Thr58, and GDP, suggesting a role of trametinib in maintaining KRASG12D in an inactive state and impede the further cell signaling. The decomposition binding free energy values illustrate amino acids' contributions to binding energy, elucidating ligand–protein interactions and molecular stability. The machine learning approach reveals that van der Waals interactions among the residues play vital role in complex stability and the potential amino acids involved in drug–receptor interactions of each complex. These details provide a molecular‐level understanding of drug binding mechanisms, offering essential knowledge for further drug repurposing and potential drug discovery. [ABSTRACT FROM AUTHOR]

Background and purpose: Sodium channel blockers (SCBs) have traditionally been utilized as anti‐seizure medications by primarily targeting the inactivation process. In a drug discovery project aiming at finding potential anticonvulsants, we have identified arbidol, originally an antiviral drug, as a potent SCB. In order to evaluate its anticonvulsant potential, we have thoroughly examined its biophysical properties as well as its effects on animal seizure models. Experimental approach: Patch clamp recording was used to investigate the electrophysiological properties of arbidol, as well as the binding and unbinding kinetics of arbidol, carbamazepine and lacosamide. Furthermore, we evaluated the anticonvulsant effects of arbidol using three different seizure models in male mice. Key results: Arbidol effectively suppressed neuronal epileptiform activity by blocking sodium channels. Arbidol demonstrated a distinct mode of action by interacting with both the fast and slow inactivation of Nav1.2 channels compared with carbamazepine and lacosamide. A kinetic study suggested that the binding and unbinding rates might be associated with the specific characteristics of these three drugs. Arbidol targeted the classical binding site of local anaesthetics, effectively inhibited the gain‐of‐function effects of Nav1.2 epileptic mutations and exhibited varying degrees of anticonvulsant effects in the maximal electroshock model and subcutaneous pentylenetetrazol model but had no effect in the pilocarpine‐induced status epilepticus model. Conclusions and implications: Arbidol shows promising potential as an anticonvulsant agent, providing a unique mode of action that sets it apart from existing SCBs. [ABSTRACT FROM AUTHOR]

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50/IC50 and KD/Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug‐target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug‐target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much‐needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Concise assembly of spirooxindoles is of great significance but a challenging task in modern organic synthesis. Described herein is an unusual base‐promoted [4+2] spiroannulation of rarely used isatin‐derived β‐silylcarbinols with o‐halogen aromatic ketones, which enables rapid and modular synthesis of six‐membered carbocyclic spirooxindoles in high yields with excellent functional group tolerance (> 50 examples). Mechanistic experiments revealed that this reaction involved a Peterson olefination, Michael addition and intramolecular C(sp3) arylation cascade. The variegated synthetic derivatization of target products and successful construction of bioactive molecules further illustrate the synthetic potential in spirooxindole‐related drug discovery. [ABSTRACT FROM AUTHOR]

The ASCPT Journal Family provides a platform for research in clinical pharmacology and translational science, along with career development and networking opportunities. Members can renew their membership, submit abstracts for the Annual Meeting, and apply for editorial positions. The organization recognizes excellence in the field through awards and supports initiatives to advance clinical pharmacology and translational science. The text features the experiences of members like Erick Velasquez, Dr. Seeprarani Rath, Hyunseo Park, and Dr. Syed Saoud Zaidi, highlighting the importance of diversity and inclusion in the scientific community. New members from diverse backgrounds and institutions worldwide are welcomed, showcasing the global reach and inclusivity of ASCPT. [Extracted from the article]

Oncogenes and tumor suppressors are well‐known to orchestrate several signaling cascades, regulate extracellular and intracellular stimuli, and ultimately control the fate of cancer cells. Accumulating evidence has recently revealed that perturbation of these key modulators by mutations or abnormal protein expressions are closely associated with drug resistance in cancer therapy; however, the inherent drug resistance or compensatory mechanism remains to be clarified for targeted drug discovery. Thus, dual‐target drug development has been widely reported to be a promising therapeutic strategy for improving drug efficiency or overcoming resistance mechanisms. In this review, we provide an overview of the therapeutic strategies of dual‐target drugs, especially focusing on pharmacological small‐molecule compounds in cancer, including small molecules targeting mutation resistance, compensatory mechanisms, synthetic lethality, synergistic effects, and other new emerging strategies. Together, these therapeutic strategies of dual‐target drugs would shed light on discovering more novel candidate small‐molecule drugs for the future cancer treatment. [ABSTRACT FROM AUTHOR]

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

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications). [ABSTRACT FROM AUTHOR]