Breast cancer is one of the most prevalent cancers worldwide, ranked as the second most diagnosed cancer and the fourth leading cause of cancer-related deaths. Despite the availability of FDA-approved therapies, limitations such as drug resistance and off-target effects highlight the need for novel, multitargeted therapeutic agents. In this study, we aimed to identify and design an in-silico promising multitarget drug for breast cancer by simultaneously targeting three critical proteins: Glucocorticoid Receptor, Estrogen Receptor-alpha (ER-alpha), and Cyclin-Dependent Kinase 2 (CDK2). FDA-approved drugs corresponding to these targets were initially subjected to multitarget molecular docking to evaluate their binding affinities. Based on this screening, the 15 highest-ranking ligands were selected and underwent molecular enumeration, resulting in the generation of 14,750 novel derivative compounds. The re-docking identified 1-((R)-2,3-dihydroxypropyl) -3-(3-((R)-1-5-methyl-1H-pyrrolo [2,3-b]pyridin-3-yl)ethyl)phenyl) urea (DdpMPyPEPhU) (Patent No. 202024101028.0) as a promising multitarget candidate. The compound exhibited enhanced binding pocket engagement through numerous stabilising interactions, including hydrogen bonds, π-π stacking, and π-cation interactions, with high docking scores (-14.869 to -4.57 kcal/mol) and favourable Molecular Mechanics Generalised Born Surface Area (MM-GBSA) energies (-72.32 to -11.97 kcal/mol). Comparative docking and pharmacokinetic analyses with standard drugs Lapatinib and Tamoxifen indicated better drug-like properties and pharmacokinetic advantages for DdpMPyPEPhU. Additional validation using Density Functional Theory (DFT) optimisation, 5 ns WaterMap analysis, and 250 ns molecular dynamics simulations under neutralised conditions confirmed structural stability and strong intermolecular interactions, supported by binding free energy calculations. Overall, our computational findings suggest that DdpMPyPEPhU is a promising therapeutic candidate for breast cancer, providing a rational basis for further experimental evaluation.

Neutropenia, characterized by a critical reduction in neutrophils, demands targeted therapeutic strategies to enhance the delivery efficiency of granulocyte colony-stimulating factor (G-CSF) specifically to bone marrow macrophages. This study focused on engineering mannose-modified poly(D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs) to achieve ligand-directed delivery of G-CSF. Mannose anchoring was achieved via Ethylenediamine (EDA)-mediated chemical ligation using N-hydroxysulfosuccinimide (NHS) and dicyclocarbodiimide as coupling agents, resulting in Mn-EDA-PLGA NPs. G-CSF-loaded and placebo NPs were fabricated through a multiple emulsion solvent evaporation method and subjected to comprehensive physicochemical characterization. The developed placebo Mn-EDA-PLGA NPs measured 199 ± 12 nm, while G-CSF-loaded Mn-EDA-PLGA NPs measured 153 ± 12.2 nm, both exhibiting a negative surface charge of − 40.07 ± 1.1 mV and − 34.9 ± 1.9 mV, respectively. Polydispersity index values were low (0.34 and 0.41), indicating uniform particle distribution. Entrapment efficiencies were significant, with the optimized G-CSF-loaded formulation achieving 72.6% drug encapsulation efficiency and drug loadings of 5 µg and 3 µg for placebo and active NPs, respectively. Scanning electron microscopy confirmed spherical morphology with smooth surfaces. Biological evaluation using scintigraphy and flow cytometry in J774.2 macrophage cells validated the targeting efficiency of mannose-modified NPs. Furthermore, molecular docking and molecular dynamics simulations substantiated the stability and interaction profile of G-CSF within the nanocarrier system. The convergence of in vitro, in vivo, and silico findings underscores the potential of Mn-EDA-PLGA NPs as a robust delivery vehicle for G-CSF, offering enhanced bone marrow macrophage targeting. This targeted approach holds promise for improving therapeutic outcomes in neutropenia by maximising drug localization and minimising systemic exposure.

ABSTRACT Lung cancer, with more than 50 approved drugs, is still the deadliest cancer, with 1.80 million annual deaths, necessitating rapid drug development, which can be accelerated by AI‐driven prediction of potent candidates. In this study, we downloaded the lung cancer BioAssay data from ChEMBL and PubChem and filtered at a 5.0 µ m threshold, yielding 4,537 and 8,661 unique active compounds, respectively, and equal inactive molecules are extracted from the big inactive compound library, totalling 26,396 unique, balanced compounds are taken for descriptor computations with QikProp and AlvaDesc software. Mean imputations and standard scaling with PCA for feature sorting, followed by three Deep Learning Models—Residual Neural Network, Feed Forward Neural Network, and Recurrent Neural Network—with an 80:20 split, 50–100 epochs, Adam optimizer, 0.001 learning rate, 32 batch size, early stopping, and ensembled (majority voting, averaging, and stacking) to enhance robustness, accuracy, generalization, stability, and confidence in predicting Activity scores from 1 to 10. A user interface is built to deploy the trained models (h5) for scoring unlabeled compounds (scores 5–10 as highly active), achieving 0.99–1.0 accuracy and F1 scores. The top predicted compound library is docked (HTVS, SP, XP, MM‐GBSA) against ALK, HSP5, KRas, MMP‐8, and tRNA DHDS2, identifying the top three multitargeted hits (PubChem CIDs: 144074375, 440810382, and 48426893) with docking scores from –10.8 to –5.6 kcal/mol and MM‐GBSA energies from –67.7 to –10.4 kcal/mol. Pharmacokinetics and DFT analyses confirmed the drug‐likeness of the compound, while 5 ns WaterMap simulations revealed implicit water roles in interactions, and 100 ns MD simulations showed deviations and fluctuations within 2 Å, with numerous intermolecular interactions. The entire in‐silico study supported and validated the deep learning predictions, identifying the computational potency of compounds against lung cancer proteins—warranting experimental validation.

Accurate cancer stage classification is crucial for tailored treatment strategies. Here, we propose a novel method utilizing Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks to classify cancer stages from single-cell RNA sequencing (scRNA-seq) gene expression data. SVM handles both linear and nonlinear tasks, while LSTM captures temporal dependencies, exploiting the rich information in scRNA-seq data. Several evaluation metrics are utilized to compare the effectiveness of the models. To overcome the sparsity and large volume of scRNA-seq data, we utilize preprocessing and dimensionality reduction methods. Different kernels in SVM, including linear, polynomial, and radial basis functions (RBF), are explored to assess their impact on classification accuracy. Experimental results indicate room for improvement, encouraging further research to enhance the accuracy of our deep learning model. This study contributes to advancing the field of cancer stage classification using scRNA-seq data and underscores the potential of machine learning and deep learning models in clinical decision-making, paving the way for more personalized treatment approaches. Both SVM-Linear (avg. accuracy = 88.46

Ovarian cancer is a major contributor to disease burden and fatalities globally, with early-stage cases posing substantial treatment hurdles due to restricted therapeutic modalities. Traditional Chinese Medicine (TCM) has gained attention for its diverse repertoire of natural compounds exhibiting possible antitumor effects. This research explores the therapeutic potential of oxypeucedanin hydrate (docking score of-9.75 kcal mol(-1)). Computational approaches were employed to assess the interaction strength between oxypeucedanin hydrate and the critical protein phosphoserine aminotransferase 1 (PSAT1) involved in ovarian cancer progression. Molecular dynamics (MD) simulations revealed the stability and conformational behavior of the compound-target complexes, mainly focusing on Trp107. Pharmacokinetic evaluation showed high intestinal permeability (QPPCaco = 468.389) and good oral absorption (83.94%). WaterMap analysis further supported its binding stability with a binding free energy (dG) of-15.41 kcal mol(-1) and a favorable entropy contribution (-30.08 kcal mol(-1)). A 100 ns MD simulation confirmed the conformational stability of the OPH-PSAT1 complex, with a mean protein backbone root mean square deviation (RMSD) of 1.70 & Aring; and ligand RMSD of 3.09 & Aring;. MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) analysis yielded an average binding free energy (dG_bind) of-63.88 kcal mol(-1), outperforming reference ligand pyridoxamine phosphate (PMP) (-48.08 kcal mol(-1)). These findings position oxypeucedanin hydrate as a novel therapeutic candidate capable of suppressing tumor growth and triggering programmed cell death in ovarian malignancies.

Huntington’s disease (HD) is a progressive, autosomal dominant neurodegenerative disorder characterized by cognitive decline, psychiatric disturbances, and motor dysfunction. HD eventually leads to severe dementia, speech loss, and complete motor incapacitation. Despite extensive research, no curative therapy exists; current treatments are limited to symptomatic relief. The molecular pathology of HD involves mitochondrial dysfunction, protein aggregation, and excitotoxicity, indicating that a multitargeted therapeutic approach may be more effective. This study aimed to identify a novel multi-target inhibitor with potential efficacy against key proteins implicated in HD pathogenesis. An in silico strategy was employed to identify a multi-target inhibitor targeting three proteins critical to HD: Kynurenine 3-Monooxygenase (KMO), Caspase-6, and Glycogen Synthase Kinase 3 Beta (GSK-3β). Structure-based drug design and virtual screening were conducted, followed by pharmacokinetic profiling, molecular docking, and molecular dynamics simulations. Further, MM/GBSA free energy calculations and WaterMap analysis were performed to evaluate binding energetics and solvent interactions. DTB-acid emerged as the most promising candidate, exhibiting favourable docking and binding energetics across all three proteins. IFD docking produced scores of − 9.03 kcal/mol (Caspase-6), − 7.33 kcal/mol (KMO), and − 7.96 kcal/mol (GSK-3β). MM/GBSA binding free energies confirmed a stable and energetically favourable association, with dG values of − 31.03, − 36.58, and − 27.15 kcal/mol for Caspase-6, KMO, and GSK-3β, respectively. WaterMap analysis further supported thermodynamic feasibility, revealing favourable hydration contributions, particularly for GSK-3β (dG = − 33.99 kcal/mol). MD simulations demonstrated stable protein–ligand complexes over 100 ns. The study underscores the potential of multitarget computational approaches in tackling complex diseases like HD. DTB-acid emerges as a promising lead molecule, meriting further experimental validation through in vitro and in vivo studies for its therapeutic potential in HD.

The nanoinformatics provides a platform to refine the nanotechnology approach by controlling the parameters based on the previous informations. Nanoinformatics helps the research community by leveraging sophisticated algorithms and complex computational modeling to predict the essential properties of nanomedicine and ensure their optimal biological interaction and performance. There are numerous potential roles of nanoinformatics in enhancing therapeutic value and preventing unpredictable toxicological pathways of nanomedicine. This review article delves into the pivotal applications of various computational tools to optimize the biological behavior of nanomedicine by controlling their physicochemical characteristics. This review thus offers an insight into adequately comprehending the in silico models such as nano-QSAR, MD simulations, CGMD and Brownian simulations to optimize nanomedicine. These tools help in product development by reducing the cost and time by controlling several biological responses of nanomedicines, including their protein interaction, mitigation, extravasation, receptor interaction and toxicological responses.

A gene regulatory network (GRN) is a group of molecular regulators that operate together and with other components of the cell to control the amounts of mRNA and protein gene expression, which in turn controls the function of the cell. Single-cell gene expression can be quantified using scRNA-seq technology. Finding genes that co-express or show associated expression patterns between cells is made possible by these high-resolution data. GRNs can be constructed with the assistance of co-expression patterns, which may indicate possible links between genes. The reconstruction of GRNs is of cardinal importance in unraveling the complicated regulatory mechanisms in biological systems at the single-cell level. In this paper, we have used the GSE81252 dataset from NCBI-GEO related to liver tissue single-cell RNA-sequencing. We applied Long Short-Term Memory (LSTM), a deep learning method, for reconstructing GRNs. We obtained an accuracy of 94.74

Pediatric pneumonia remains a leading cause of morbidity and mortality among children worldwide, necessitating the exploration of novel therapeutic interventions. Traditional Chinese Medicine (TCM) offers a rich repository of natural compounds with potential therapeutic benefits. In this study, we explore the role of the TCM-derived compound ADHPE ([(1S,3S)-3-acetoxy-5-(3,4-dihydroxyphenyl)-1-[2-(3,4-dihydroxyphenyl)ethyl]pentyl]) as a stabilizer of 14-3-3σ and p65 complex in pediatric pneumonia through a comprehensive in silico approach. Using virtual screening and molecular docking, we screen the TCM drug library and assess the binding affinity of ADHPE to key protein targets implicated in the pathogenesis of pediatric pneumonia-associated acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The stability and dynamics of the drug-target complexes are further evaluated through molecular dynamics (MD) simulations, providing insights into the interaction mechanisms at an atomic level. Additionally, we perform ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis to predict the pharmacokinetic and safety profiles of ADHPE. Further, MM\GBSA, WaterMap, and Piper analyses were conducted to confirm the results. The findings from this study may pave the way for the development of effective TCM-based therapies for pediatric pneumonia, offering a promising alternative to current treatment modalities.

Novel Schiff base metal complexes diaqua(4-chloro-2-{[(4-(dimethylamino)phenyl)methylene]amino}phenolato)M(II) ion, (M = copper/cobalt/nickel) have been synthesized using the ligand 4-chloro-2-((4-(dimethylamino)benzylidene)amino)phenol, which was prepared by reacting 2-amino-4-chlorophenol with 4-(dimethylamino)benzaldehyde in a molar ratio of 1:1. The synthesized compounds were characterized based on elemental analysis, FTIR, UV-vis., H-1 and C-13 NMR, powder XRD, mass spectrometry and magnetic studies. Spectroscopic investigations demonstrated that the metal atom coordinates to a bidentate Schiff base ligand via the azomethine nitrogen and phenolic oxygen. The appropriate molecular geometry for each metal complex has been proposed through the analysis of spectroscopic and analytical data, Specifically, tetrahedral geometry has been proposed for Co(II), Ni(II), and square planar geometry for Cu(II) complex. Theoretical calculations employing Density Functional Theory (DFT) validated the experimental results, elucidating optimized geometries, frontier molecular orbitals, natural bond orbital distributions, molecular electrostatic potentials, non-linear optical properties, IR vibrational modes, and UV absorbance profiles. Additionally, the compounds' biological efficacy was validated through molecular docking against receptors for Gram(+ve) bacteria, Bacillus subtilis (PDB ID: 5H67), Staphylococcus aureus (PDB ID: 3TY7) and Gram(-ve) bacteria, Escherichia coli (PDB ID: 3T88), Proteus vulgaris (PDB ID: 5I39). The outcome revealed that Co(II) complex exhibited highest binding energy with Escherichia coli and Bacillus subtilis while the ligand with Proteus vulgaris (5I39) and Staphylococcus aureus (3TY7). Further the study was extended to the MD Simulation in water for 100 ns to evaluate the binding affinities and its stabilities by analyzing the deviation, fluctuations, and intermolecular interactions. The research presented here showcase the potential of employing a Schiff base ligand and its Co(II) complex in the synthesis of novel, highly potent antibacterial agents to combat emerging diseases, which holds considerable importance in the field of pharmaceutical science-however, in-vitro studies are needed to validate the results.

Cancer has become a prevalent disease that imposes a huge burden on society and the scientific community, and the worst-case scenario has evolved nowadays due to its resistance profiles. Lung cancer comes first in diagnosis and death and is gender-neutral, causing 1.8 million deaths yearly. In this study, we have collected 24 target proteins of lung cancer that are actively participating in its development, and the idea behind it was to identify a drug candidate that can show multitargeted efficacy against all. We performed molecular docking with HTVS, SP and XP sampling algorithms and MM\GBSA-based pose fileting, which helped to identify Theodrenaline (DB12927) as a multitargeted inhibitor with docking scores ranging from -5.1 to -13.6 Kcal/mol. Interaction fingerprint analysis identified 30LEU, 23VAL, 20LYS, and 17ASP as key residues, emphasizing hydrophobic interactions. Compared to Theodrenaline, the FDA-approved drug Crizotinib showed less promising results. Pharmacokinetic assessments, DFT calculations, and 5 ns WaterMap computations supported Theodrenaline's drug-like properties and computational efficacy. Additionally, a 100 ns MD simulation using the SPC water model (NPT ensemble) showed minimal deviations (<2 Å) and strong intermolecular interactions, while Crizotinib exhibited lower stability and weaker interactions. Theodrenaline exhibited notable cytotoxic effects on the A549 cancer cell line, with ∼58 % and 41 % cytotoxicity observed after 24 h and 48 h of treatment at a 10 μM concentration, respectively. Despite some cytotoxicity on normal cells at higher concentrations, theodrenaline was less toxic to these cells than cancer cells, as confirmed by microscopic analysis. Theodrenaline also demonstrated potent antioxidant activity, surpassing Paclitaxel and ascorbic acid, with 62 % DPPH activity at 10 μM. Furthermore, theodrenaline downregulated NFκB p65 protein expression by 35 % at 1 μM after 24 h, showing similar effectiveness to Paclitaxel at lower concentrations. Comprehensive computational and experimental studies support Theodrenaline as a promising multitargeted drug that outperforms FDA-approved compounds in overcoming lung cancer resistance-however, in vivo studies are recommended for further validation.

Diagnostic biomarker for Ebola Virus Disease (EVD) and its overlapping diseases, which include COVID-19, monkey pox, AIDS and uveitis, is of interest. The gene-disease association network was built by using human microarray datasets from the GEO database. The comparison based on Jaccard's similarity index showed 27 intersected differentially expressed genes (DEGs), with AIDS having the highest genomic association with EVD. Gene-disease interaction analysis revealed Metastasis-associated lung adenocarcinoma transcript-1 (MALAT1) and histone cluster 1, H2bc (HIST1H2BC) as the most relevant diagnostic biomarkers. Thus, the dg IDB database provided potential for drug repurposing, targeted therapy and broad-spectrum antiviral applications.

Lung cancer (LC) remains one of the leading causes of cancer-related deaths globally, with both small and non-small cell lung cancer contributing to an estimated 1.80 million deaths annually. Drug resistance in LC treatment causes around 700,000 deaths globally and poses major economic challenges. While nearly 100 FDA-approved LC drugs exist, most target single proteins or pathways, making resistance more likely. There is an urgent need for multitargeted drugs that inhibit multiple pathways to reduce resistance and side effects. In this study, we examined 34 proteins from various LC-related pathways and prepared them for screening against the DrugBank library. Using high-throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP) docking algorithms in Glide, we identified Imidurea (DB14075) as a promising multitargeted inhibitor. Docking scores ranged from -5.507 to -12.155 kcal/mol, and poses were refined through MM/GBSA calculations. Molecular fingerprinting was performed to analyse interaction patterns, and DFT and pharmacokinetics were assessed. Bonding angles were optimised to evaluate relative binding energy, followed by 100 ns MD simulations in water to assess stability and effectiveness. Imidurea showed strong multitargeted inhibitory effects across all 34 LC proteins, with docking scores between -5.507 and - 12.155 kcal/mol. MD simulations further supported its stability in binding to these targets. In MTT assays on A549 cells, Imidurea demonstrated dose-dependent anticancer effects, causing significant cell cycle arrest and cell death at 100 μg/mL. Imidurea is an affordable compound with promising multitargeted anticancer potential, particularly beneficial for developing nations and underprivileged groups. While these computational and in vitro MTT assays are encouraging, further detailed in vitro and in vivo studies are necessary to validate their efficacy and safety before clinical application.

Despite the availability of Pap tests and HPV vaccines, Cervical Cancer continues to be a significant factor contributing to women's deaths. It poses severe consequences to women's health. The disease's severity lies in its potential to progress silently in its early stages, mainly detected in its advanced stage, and clinical treatment is challenging due to drug resistance. This study aims to identify multitargeted lead molecules based on the interactome of Cervical Cancer-related crucial genes, which can help develop drug-resistant therapies. We have considered 9 crucial Cervical Cancer genes, namely BUBR1, CCNB1, FEN1, MAD2, MCM10, MCM6, ITGB8, POLE, and TPX2, to perform gene network analysis and Gene Ontology enrichment studies to identify the potential hub genes and their role. Further, we performed multitarget screening using multisampling algorithms HTVS, SP, and XP to screen the protein products of the 9 genes for their binding affinity for the FDA-approved drugs library. The binding affinities of the compounds were evaluated using MM\GBSA that identified multitargeted potential inhibitor as a Levophed for Cervical Cancer, and the docking results showed a range of MM/GBSA scores, varying from -8.35 to -5.38 kcal/mol for docking, and -43.41 to -19.37 kcal/mol for MM/GBSA scoring. The protein residues that interact the most with Levophed are ALA, THR, ILE, ASN, GLY, ASP, LEU, LYS, VAL, GLN, PRO, CYS, GLU, and TYR. The pharmacokinetic properties and WaterMap computations also support the idea that the compound can potentially become a drug candidate. Furthermore, all 9 complexes were simulated for 100ns, resulting in cumulative deviation and fluctuation of <2 Å, with many intermolecular interactions and binding free energy computations supporting the studies. The study shows that Levophed could treat Cervical Cancer without encountering drug resistance- however, experimental studies are needed to confirm the accuracy.

With the escalating number of vehicles and the lack of parking spaces, the issue of parking has become a significant problem in major cities as it is a daily occurrence for educational institutions, companies, and government facilities, resulting in fuel wastage and time inefficiencies. In their work lives, employees often face problems when parking their cars in the work parking area. Finding a space for their vehicle can take a lot of time and effort, leading to late arrival for work. On the other hand, security guards have difficulty entering their employees’ cars. In this context, our proposed system attempts to address this pressing issue, which consists of two parts: one is a camera at the parking gate that recognizes the license plate using the Automatic Number Plate Recognition (ANPR) algorithm, where the camera captures the license plate and outputs the plate number using the optical character recognition (OCR) technique. After that, the resulting data is cross-referenced with database records for seamless entry authentication. This eliminates the need for security personnel to verify vehicle identities or stickers manually, streamlining access procedures. The second part is a camera in the car parks that distinguishes between vacant and available parking spaces and stores the data collected by the camera in the centralized database, enabling the real-time display of the nearest available parking spots on digital screens at entrance gates, significantly reducing the time and effort spent in locating parking spaces. Through this innovative solution, we aim to enhance urban mobility and alleviate the challenges associated with urban parking congestion, thereby resolving the problem of intelligent parking for smart cities with the help of machine learning.