Naturally derived Erythrinin C targets γ-secretase signaling to suppress triple-negative breast cancer progression and reverse paclitaxel resistance.

Repurposing drugs for EGFR-targeted cancer therapy: An in silico and in vitro study with pharmacophore-based insights.

Exploring traditional Chinese medicine for antiviral drug discovery: A computational approach to combat human metapneumovirus (HMPV).

Ligand-Based Design of Novel Thiazole-Quinazolinone Hybrids with Dual Antimicrobial and Anti-Virulence Activity Targeting Staphylococcal Sortase A.

The ATP-dependent bile salt export pump (BSEP) is a transporter responsible for moving bile salts from hepatocytes into bile canaliculi. Inhibition of BSEP is a known risk factor of cholestatic-drug-induced liver injury (DILI) probably caused by the accumulation of toxic bile salts in the liver. Since DILI is a major cause for attrition during drug development and postmarketing withdrawal or black box warnings, an IC50 of BSEP higher than 25 μM is advised. Here, we describe the investigation of several in silico approaches to map potential BSEP inhibitors. A consensus machine learning classification model is presented, which accurately predicts BSEP inhibitors and can assist in the prioritization of in vitro testing. An applicability domain was derived for the model, and it was validated by in vitro measurements of predicted BSEP inhibitors where the model was able to correctly flag eight out of 11 compounds.

ALOX15 is a key regulatory enzyme in multiple pathological processes including inflammation, cancer, and cardiovascular disease, rendering the development of potent inhibitors of this enzyme of significant clinical importance. This study aims to screen and optimise novel ALOX15 inhibitors by integrating multiple computational chemistry and rational drug design approaches. First, we constructed a 3D-QSAR pharmacophore model based on 28 known inhibitors to screen the Comprehensive Marine Natural Products Database (CMNPD), which contained 25,224 compounds. Combining machine learning and molecular docking methods, we preliminarily identified three molecules. Through ADMET analysis and scaffold hopping optimisation, we obtained three candidate compounds exhibiting both high binding affinity and favourable pharmacokinetic properties. Toxicity predictions indicated all compounds fell within the confidence interval for predicted non-toxicity or low toxicity. Molecular dynamics simulations further confirmed strong binding affinity between the candidates and ALOX15. Finally, off-target analysis identified two novel ALOX15 inhibitors, providing potential candidate molecules for subsequent development of ALOX15-targeted therapeutics.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction, amyloid-β aggregation, mitochondrial stress, and aberrant kinase activity. Carotenoids, naturally occurring pigments with antioxidant and neuroprotective properties, have emerged as promising candidates for AD intervention. In this study, we performed a systematic stepwise computational screening of a large carotenoid library (n = 1191) to identify multitarget candidates against AD-related proteins. The workflow consisted of predefined ADMET filtering (oral absorption > 90%, Caco-2 > 0.9, logBB > -1, and absence of major CYP inhibition and toxicity alerts), reducing the dataset to 61 compounds, followed by multi-target molecular docking against AChE, BChE, BACE-1, MAO-B, and GSK3-β. Compounds were ranked using an aggregated mean docking score across all five targets, and the top-performing candidate was subjected to detailed mechanistic analyses. Hopkinsiaxanthin emerged as the highest-ranked multitarget carotenoid and was further evaluated using frontier molecular orbital (FMO) analysis, pharmacophore modeling, 100 ns molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and per-residue decomposition. Docking predicted favorable estimated binding affinities toward all targets. MD simulations confirmed stable receptor-ligand complexes with low RMSD values (0.278-0.285 nm). MM/PBSA analysis indicated favorable binding free energies, particularly for GSK3-β (-22.73 kcal/mol) and AChE (-21.50 kcal/mol). Per-residue decomposition identified key hotspot residues driving stabilization. Overall, this structured computational framework identifies Hopkinsiaxanthin as a promising multitarget scaffold and supports its prioritization for experimental validation in AD models.

De novo design methodologies have the potential to significantly enhance the exploration of chemical space in the search for promising ligands featuring novel chemotypes. This exploration can be directed through various computational strategies. 3D pharmacophore models, which represent the interaction patterns critical for protein-ligand recognition, can serve as valuable tools for the design of novel compounds. A common limitation of many generative approaches is the low synthetic feasibility of the generated molecular structures. In the present study, we developed a method capable of controllably generating compounds with a relatively high degree of synthetic accessibility by leveraging the CReM framework, while explicitly conforming to a specified 3D pharmacophore model. Evaluation of this approach across a diverse set of protein targets and pharmacophore models of varying complexity demonstrated its effectiveness and highlighted its advantages over the PGMG method, which employs a deep neural network architecture to generate ligands that may exhibit desired 3D geometries upon embedding. The proposed method has been implemented as an open-source tool, CReM-pharm, available at https://github.com/ci-lab-cz/crem-pharm.

Nowadays there are so many types of diseases that are causing to human and animal health. This disease or disorder makes human health very severe, lethal, and life-threatening to survive. Several troubling diseases make the body's condition abnormal or cause a disturbance in the function and structure of the body or organ systems. It states that the person is ill or sick which is characterized by the manifestation or symptoms which may be seen internally or externally. To overcome these manifestations there are some specific substances or molecules are used. These substances are called drugs and can cure various diseases or disorders. A drug compound may be natural or synthetic obtained from a plant, animal sources, or any chemical compound which can treat and prevent diseases. There are some modern methods to produce a drug. To discover a new drug is called a drug discovery which saves time and trial discovery takes a lot of time for selecting, researching, optimization, of a drug. These drugs are beneficial and lifesavers. Several days or years pass or are required to discover a new molecule or to design a single molecule. Drug discovery is the research of new compounds and new moiety. Drug design is a very convenient and time-saving method for drug discovery. Drug design is a type of invention and it is a computational method and it is an important part of drug discovery. There are two techniques of drug design ligand-based and receptor-based drug design. Computer-aided drug design is a computational method in which there is a prediction of the structure and molecules by a mathematical equation. In LBDD QSAR & Pharmacophore model is there and in RBDD Molecular Docking & De Novo Design is present. In QSAR there are of the efficacy of a biologically active compound. It is a mathematical relationship that identifies and quantifies the molecule. These methods can be applied and then virtual screening. Then the identification of the molecule binds to the binding site or receptor and gives its action.

Exploration of a Novel Imidazo[4,5-b]pyridine-dihydropyrimidinone hybrids as aurora kinase a inhibitors: integrated pharmacophore modelling, docking and simulation approaches.

Abstract: The drug discovery landscape and neurodegenerative disease diagnostics are expe-riencing a paradigm shift with the integration of artificial intelligence (AI) and machine learn-ing (ML) technologies. This review provides an elaborate discussion of AI-based approaches across all stages of computer-aided drug design (CADD), including virtual screening, peptide synthesis, pharmacophore modelling, quantitative structure–activity relationships (QSAR), and drug repurposing. Particular emphasis is placed on Alzheimer's disease (AD), a multifac-eted neurodegenerative disorder, with AI enabling early diagnosis, patient stratification, and subtype classification to support precision medicine. The review highlights recent developments in supervised and unsupervised learning, omics integration, and the increasing importance of explainable AI (XAI) in addressing the “black box” limitations of traditional AI models. We categorize and assess major AI platforms and tools by their scope, stage of implementation, and form of explainability, offering a practical framework for their application in pharmaceutical and clinical research. The combined use of deep learning, quantum simulation, and virtual reality is also discussed in the context of de novo drug design, compound screening, and toxicity prediction. With con-tinued advances in AI, interdisciplinary collaboration and ethical oversight are essential to translate these digital innovations into effective, safe, and personalized therapies. This review serves as an all-encompassing guide for researchers, clinicians, and developers working at the interface of AI and drug development, particularly those addressing the unmet challenges in Alzheimer's disease. As outlined in the abstract and reflected in the article title, this review bridges AI-driven drug discovery and Alzheimer’s diagnosis, with specific empha-sis on explainable models for next-generation therapeutics. The subsequent sections expand on these themes, offering a structured synthesis of tools, applications, and methodological frameworks.

Glioblastoma (GBM) may cause neurological dysfunction, leading to significant physical and mental distress and complicating oncological treatment as well as clinical care. Agents that combine anti-GBM and neuroprotective activities may offer a promising solution to this challenge. Andrographolide exhibits broad-spectrum anti-tumor effects, blood-brain barrier (BBB) permeability, and neuroprotective properties, making it a promising candidate for GBM treatment, though its nephrotoxicity warrants caution. Many andrographolide derivatives with enhanced therapeutic efficacy and reduced nephrotoxicity have been reported. Therefore, this study developed validated pharmacophore models using these andrographolide derivatives to identify natural products with dual anti-GBM and neuroprotective effects, and employed network pharmacology and molecular dynamics (MD) simulations to explore their potential mechanisms of action. Mevastatin was identified as a promising hit compound, exhibiting low cytotoxicity in HK-2 cells while persistently inhibiting A172 cell growth and migration (IC50 = 4.639µM). It also demonstrates BBB penetrability and potential neuroprotective effects. Network pharmacology analysis revealed that beyond HMGCR, mevastatin may directly interact with 12 additional targets relevant to GBM treatment. MD simulations elucidated its binding mechanisms to MAPK1, MDM2, MMP2, and GRB2.In summary, starting from a series of multi-target and multifunctional natural product derivatives sharing the same core scaffold, this study identified a small molecule that exhibits higher efficacy, lower toxicity, and potential multifunctional activity compared to the reference compound, thus proposing a novel strategy for the discovery of multi-target, multifunctional therapeutic agents.

Design-led synthesis and multidimensional evaluation of novel thiadiazoles as multitarget anti-Alzheimer agents: kinetics, DFT and in silico mapping.

Adenosine triphosphate (ATP) synthase in Mycobacterium tuberculosis (Mtb) is essential for energy metabolism through oxidative phosphorylation, where ATP is synthesized from ADP. This enzyme supports bacterial survival during both active growth and dormancy, enabling Mtb to persist under stressful conditions. During dormancy, Mtb enters a non-replicating, drug-tolerant state that reduces the effectiveness of many antibiotics. Inhibition of ATP synthase therefore disrupts ATP-dependent survival mechanisms in Mtb. Although this target has been clinically validated by bedaquiline, the emergence of resistance and the limited chemical diversity of reported inhibitors indicate a clear need for new ATP synthase-targeting compounds. In this study, we employed an integrative pipeline combining structure-based pharmacophore modeling, artificial neural network-driven quantitative structure-activity relationship (ANN-QSAR) modeling, and absorption distribution metabolism excretion and toxicity (ADMET)-based pharmacokinetic filtering strategies to screen an antituberculosis-targeted library of approximately 4200 molecules from the Life Chemicals database. Initial screening identified 8 hit molecules characterized by key molecular features previously highlighted as positive contributors in both Shapley Additive Explanations (SHAP) and Pearson correlation analyses, including SubFP1 (primary carbon), SubFP88 (carboxylic acid derivative), SubFP143 (carbonic acid derivative), SubFP9 (alkyl fluoride), SubFP137 (vinylogous ester), SubFP184 (heteroaromatic), SubFP26 (tertiary aliphatic amine), and SubFP171 (aryl chloride). Molecular docking and molecular dynamics simulation studies (200 ns) further highlighted molecules F0526-1306 and F0526-1309 as the most promising candidates. Subsequent antimycobacterial inhibition assays demonstrated that both molecules significantly reduced mycobacterial biofilm formation. In addition, toxicity evaluations using a zebrafish model confirmed the safety and favorable tolerability of these molecules, supporting their potential as viable candidates for further preclinical and in vivo drug development studies.

Drug-target interactions (DTIs) are fundamental to drug discovery, development, and repositioning. However, experimental methods for DTI identification are often constrained by high costs, time demands, and scalability issues, prompting a shift toward computational approaches. This review systematically explores recent advancements in computational DTI prediction, encompassing ligand-based, target-based, network-based, machine learning (ML), deep learning (DL), and hybrid multi-omics models. Ligand-based techniques, such as QSAR and pharmacophore modeling, offer structure-activity insights but require known ligands. Target-based methods rely on molecular docking and binding site prediction, yet often suffer from incomplete or unknown protein structures. Network-based strategies utilize bipartite and heterogeneous graphs integrated with protein-protein interaction (PPI) networks to infer novel DTIs. ML and DL methods especially graph neural networks and Transformer-based models have significantly improved prediction accuracy by leveraging chemical, biological, and omics features. Notably, hybrid models that integrate genomics, transcriptomics, proteomics, and interactomics data offer a systems biology perspective, enabling context-specific and personalized predictions. Key databases, like DrugBank, ChEMBL, and Binding DB, and computational tools such as Deep Purpose, NeoDTI, and FusionDTI, exemplify the latest advances in DTI prediction. Validation strategies are discussed through contemporary case studies. While substantial progress has been made, key challenges remain, including data sparsity, model interpretability, and generalization. Looking forward, emerging trends such as federated learning, AlphaFold-based docking, and quantum simulations are poised to further transform the field. This review emphasizes the importance of interdisciplinary integration and ethical frameworks, charting a roadmap for future DTI research and its translational applications in precision medicine.

The main dietary triglyceride hydrolyzing catalyst, and a long time pharmacological parameter of decreased dietary caloric intake, is pancreatic lipase (PL). Recent progress in structural biology, high resolution crystalography and computational models has given a new understanding of the catalytic triad of PL and interfacial activation, lid dynamics and stabilization of colipase dependent. These mechanistic underpinnings have facilitated more rational search of the varied classes of inhibitors including covalent β-lactones to reversible natural products (flavonoids, aurones, chalcones) and contemporary synthetic scaffolds like thiazolidinedione, triazole, and multi-target hybrid chemotypes. The mechanism by which the inhibitors interact with the hydrophobic acyl-binding tunnel, oxyanion hole, and aromatic platform around Ser152 is now understood using quantitative structure-activity correlations, molecular docking, molecular dynamics simulations, and pharmacophore models. The new approaches to medicinal chemistry, such as allosteric inhibition of lid movement, partial inhibition to enhance the safety, the investigation of non-2-lactone electrophiles, and AI-assisted scaffold discovery provide avenues to effective, yet safer inhibitors. The enzymatic mechanism, structural biology, SAR trends, and computational methods have been incorporated in this review to present a single framework in designing next-generation pancreatic lipase inhibitors.

Gram-negative pathogens are difficult to treat because their outer membrane, enriched with lipid A-anchored lipopolysaccharide, serves as a protective barrier to many antibiotics. LpxH, an essential dimanganese hydrolase in lipid A biosynthesis, represents a promising antimicrobial target, but its distinct L-shaped binding pocket has limited inhibitor development, with only the sulfonylpiperazine chemotype reported to date. To broaden the chemical space, we developed a multistage virtual screening workflow combining HipHop-based pharmacophore modeling, ROCS-based query matching, and FRED docking. This pipeline identified F523-0608, an acetylpiperazine-containing compound, as a moderate Klebsiella pneumoniae LpxH (KpLpxH) inhibitor. Substructure searching and optimization yielded compound 7, a potent inhibitor (IC50: 0.17 μM) with moderate antibacterial activity (MIC: 5.3 μg/mL). The crystal structure of the KpLpxH-compound 7 complex revealed its binding mode, validating virtual screening analysis. These studies establish acetylpiperazine derivatives as a new class of LpxH inhibitors and provide a foundation for future antibiotic development.

Diabetes is the most prevalent metabolic disorder distinguished by increased blood glucose levels. Unfortunately, none of the marketed treatments can cure the ailment. Glucokinase has recently emerged as a novel target acting in both the liver and the pancreas. The activity of this novel protein in the liver is regulated by a protein called glucokinase regulatory protein (GKRP). The study aimed to identify novel GKRP modulators and evaluate their potential as antidiabetic agents using a comprehensive approach incorporating in silico, in vitro, and in vivo methods. In the quest for novel GKRP modulators, pharmacophore models were developed using Hypogen and HipHop methodologies. The optimal pharmacophore hypothesis, featuring 1HBA, 1HY, 1HBD, and 1RA, demonstrated a root mean-square deviation of 0.88 Å and a high correlation coefficient of 0.88. Fisher’s randomization and Cat Scramble tests confirmed the statistical validity of the models, with a 95% confidence level. The validated pharmacophore model was employed in a virtual screening of the NCI database, resulting in the retrieval of key hits, including NCS 1972 and NCS 80683. These compounds were subjected to docking studies and in vitro enzyme-based GKRP modulatory assay, revealing favorable binding interactions with the GKRP active site and IC50 of 1.60 and 3.04 nM, respectively. In vivo evaluations showed that NCS 1972 (2.5 mg/kg) significantly improved lipid profiles, reduced liver hypertrophy and adiposity, and enhanced body mass index, 0.27 ± 0.02 g/mm, 0.21 ± 0.12g/mm, 0.70 ± 2.82 g/cm2 respectively, compared to the HFD-STZ group. Histopathological analyses demonstrated substantial cellular restoration in pancreatic and liver tissues, with NCS 1972 exhibiting superior efficacy over NCS 80683. Additionally, gene expression studies revealed that both compounds corrected HFD-STZ-induced dysregulation of glucose metabolism and inflammatory markers. These findings underscore the significant therapeutic potential of NCS 1972 in mitigating diabetes, suggesting its promise as a novel antidiabetic agent.

Organophosphorus (OP) nerve agents inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) by phosphylating the catalytic serine. Oxime reactivators can restore enzymatic activity by a nucleophilic attack of the oximate anion on the phosphorus center of the enzyme–OP conjugate; however, clinically used oximes show agent- and enzyme-dependent performance and are particularly challenged by A-series compounds. Here, an in silico strategy is presented to identify candidate antidotes for OP poisoning, including A-series agents. Pharmacophore models are generated from benchmark/template oximes. Pharmacophore-based virtual screening is used to retrieve hit-like scaffolds from the available chemical space, after which selected hits are converted into oxime analogs. Template oximes and newly designed oximes are then docked into the active site of AChE or BChE inhibited by specific nerve agents. The predicted reactivation potential is assessed using mechanistically motivated geometric criteria derived from the accepted reactivation hypothesis, including the distance between the oximate oxygen and the phosphyl phosphorus and the attack angle, relative to the catalytic serine Oγ. This workflow enables a controlled, pairwise comparison of new oximes against their corresponding template oximes for each enzyme–agent combination, providing a rational basis for prioritizing candidates for synthesis and experimental validation. Using the described workflow, we identified a hit compound with the potential to act as an antidote against A-series nerve agent A-230 poisoning.

ATP-citrate lyase (ACLY) is an upstream enzyme involved in fatty acid synthesis, cholesterol metabolism, and histone acetylation. Therefore, selective inhibition of ACLY represents a promising strategy for the treatment of dyslipidemia and various cancers. Recently, the cryo-EM structure of the ACLY complex with the allosteric inhibitor NDI-091143 has been reported, providing an opportunity to discover new potent inhibitors of this emerging target. In this in silico study, we report structure-based models that were rigorously developed and evaluated using reported allosteric inhibitors of ACLY. The pharmacophore model (ROC-AUC = 0.85, GH = 0.78, and EF1% = 49.18) and the molecular docking model (RMSDredock = 0.884 Å and ROC-AUC = 0.95) were applied to virtual screening of the ZINC15 library. During hit selection for further evaluation by molecular dynamics simulations, post-docking analysis was performed based on docking scores (ΔGdock) alone and in combination with the Tanimoto similarity coefficient of protein-ligand interaction fingerprints (TcIFP). The combined ΔGdock and TcIFP approach enabled the identification of four out of five selected top hits with binding free energies more favorable than that of the reference compound NDI-091143, supporting their potential as allosteric ACLY inhibitors. These compounds may be subjected to further experimental evaluation to confirm their biological activity. In addition, the workflow developed in the present study may provide a basis for future discovery and optimization of allosteric ACLY inhibitors.