Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with approximately 25–30% of cases characterized by HER2 overexpression. Although HER2-targeted therapies have shown significant clinical benefit, the emergence of acquired resistance limits their long-term efficacy. To address this challenge, in the present study we have developed the framework that integrates machine learning-based screening, molecular docking and molecular dynamics simulations for the discovery of potential HER2 inhibitors. Five different ML classifiers including, Random Forest (RF), Gradient Boosting (GB), Extra Trees classifier (ET), Adaptive Boosting (AdaBoost) and Hist Gradient Boosting (HGB) were used to develop the predictive models and evaluated. Among them, the GB model outperformed other models with an accuracy of (87.05%) and AUC of (0.94) and subsequently employed for ML based screening of phytocompounds. Further, molecular docking identified Zhankuic acid C, Hispidulin, and 5,7,3′,4′-tetrahydroxyisoflavone as the top compounds, with binding affinities of − 13.04, − 11.24, and − 11.19 kcal/mol, respectively. These compounds are further evaluated through Lipinski’s RO5, Toxicity, cDFT and MD simulations. MD simulations also confirmed the stable binding of these compounds within the HER2 active site. Collectively, these findings highlight the identified phytocompounds as promising scaffolds for the development of novel HER2-targeted therapeutics.

Breast cancer continues to be a major global health concern, and the need for targeted therapeutic options remains urgent. Janus kinase 1 is an important signalling protein involved in pathways that drive tumour progression. Inhibiting this kinase may provide a promising route to slow or alter disease development. Marine natural compounds contain diverse chemical scaffolds that often exhibit strong bioactivity. This study investigates the South African Natural Compounds Database to identify potential JAK1 inhibitors through a structured computational screening approach. A library of 1,012 marine derived compounds was screened using virtual screening and molecular docking to identify candidates with high affinity toward the JAK1 active region. Cross docking was performed to confirm target preference. Pharmacokinetic and toxicity predictions were generated using ADMET tools. Chemical reactivity was assessed through conceptual DFT calculations. The top hits were evaluated using 200 nanosecond molecular dynamics simulations in order to analyse stability, binding consistency and dynamic behaviour. The PASS server was used to predict potential pharmacological activities of the shortlisted compounds. All computational analyses were performed in parallel with the approved JAK1 inhibitor abrocitinib, which served as a reference standard for comparative evaluation. Four compounds showed notable docking scores and favourable interaction profiles. Among them, SANC00125 and SANC00136 demonstrated the highest binding stability during simulations, maintaining consistent interactions within the JAK1 pocket. Both compounds displayed favourable ADMET characteristics, low predicted toxicity, acceptable reactivity profiles and promising PASS predicted antineoplastic activity. These findings indicate strong computational support for predicted JAK1 inhibitory potential. The combined computational analysis highlights SANC00125 and SANC00136 as promising putative inhibitors of JAK1. Their stability, predicted safety and potential biological activity support further exploration in laboratory models. These results suggest that marine natural compounds may serve as valuable resources for developing targeted strategies against breast cancer.

Coastal environments are major sinks for plastic waste, yet the adaptive strategies and bioremediation potential of their native microbial communities remain largely unexplored. This study employed genome-resolved metagenomics to characterise the microbial community from a heavily plastic-polluted coastal sediment in India. We recovered 53 high and medium-quality metagenome-assembled genomes (MAGs), revealing a diverse community dominated by Bacteria (51 MAGs) and Archaea (2 MAGs). Taxonomic analysis highlighted significant novelty, with 88.7% of MAGs unclassified at the species level and nine confirmed as potentially novel species via digital DNA-DNA hybridisation. Functional profiling demonstrated a community well-adapted to the dynamic coastal setting, with broad tolerances for fluctuating pH, salinity, and temperature. The MAGs exhibited robust potential for driving key biogeochemical cycles, particularly those of carbon, nitrogen, and sulfur, with specific taxa identified as central metabolic hubs. Metabolic modelling revealed a complex network of interdependence, with Pseudoalteromonas tetraodonis(M1_bin.31) acting as a primary metabolite provider. Notably, 14 enzymes associated with the degradation of synthetic plastics, including PET, PLA, PEG, and PHB, were identified across multiple MAGs. Overall, our findings illuminate the genomic blueprint of microbial diversity and function in polluted marine sediments and provide a valuable resource for developing microbe-based strategies for plastic bioremediation. Future work will focus on heterologous expression and functional characterisation of the identified enzymes to assess their degradation potential, further advancing sustainable approaches to environmental restoration.

A low-molecular-weight cytoplasmic protein called heart-type fatty acid-binding protein (H-FABP) is released by cardiac myocytes during an ischemic episode. The normal serum level of FABP is around 3.5 ± 0.4 ng/ml (males), 3.9 ± 0.4 ng/ml (females), while the cutoff value of H-FABP in case of acute myocardial infarction (AMI) is 21.85 ng/ml within 3 hours. In case of angina pectoris, ischemic patients can be distinguished from non-ischemic patients and treated appropriately by detecting the presence of serum H-FABP in a point-of-care (POC) diagnostic approach. As the mortality rate due to coronary heart disease is on the rise worldwide, serum H-FABP levels can act as a diagnostic marker to assist in the prediction of probable cardiac arrest. In the present work, a small peptide against H-FABP has been screened by phage DNA library screening, which can be used for the development of a rapid and accurate diagnostic system for serum H-FABP. The cloning, expression and purification of the H-FABP protein in bacteria and screening of a 12 amino-acid long screened peptide with moderate affinity for binding to H-FABP is reported. The interaction of the said peptide with H-FABP was confirmed by in-silico methods like molecular docking and molecular dynamics simulation. The affinity of the peptide binding to H-FABP was assessed using Isothermal Titration Calorimetry (ITC). A spontaneous process is indicated by ΔG (-7.98 kcal mol−1) values, but an endothermic interaction activity is implied by positive ΔH (3.11 kcal mol−1) values. In a futuristic approach, this peptide can be exploited for the development of an accurate and early detection technique for heart ischemia.

The magnitude of people acquiring respiratory allergies by pollen allergens is on the rise worldwide. The pollen from Parthenium hysterophorus is a proven allergen, and its allergens trigger allergic rhinitis and bronchial asthma in atopic individuals. Despite several clinical reports available worldwide, substantive characterization of allergens of P. hysterophorus has been lacking. This study aimed to analyse the molecular, structural, and immunological characterization of P. hysterophorus pollen allergens. A total of 484 patients with allergic rhinitis and bronchial asthma were screened for sensitization to P. hysterophorus by skin prick test. Proteins from pollen of P. hysterophorus were extracted. Immunoblotting and immunoinhibition assays were carried out using sera from sensitized patients to identify the specific allergen. Molecular characterization of the specific pollen allergen was carried out by sequencing, and its structural details were analysed using bioinformatic tools. Out of the eighteen patients who tested positive by skin prick test (SPT), only five had a history of direct exposure to P. hysterophorus with significantly elevated levels of total IgE. Immunoblotting and immunoinhibition analysis of P. hysterophorus antigenic extract revealed that a 40 kDa protein was allergenic. The molecular characterization and in silico analysis revealed that the allergen was pectin methylesterase. Sensitization to P. hysterophorus pollens was evident by SPT in patients with allergic rhinitis and bronchial asthma from Puducherry. Immunoblotting and immunoinhibition studies in the sensitized patients revealed that a 40 kDa protein was allergenic. Using molecular and bioinformatics tools the allergenic protein was identified as pectin methylesterase.

Plastic pollution is an escalating environmental concern, particularly in coastal regions where sediments serve as long-term sinks for plastic debris. Despite this, the microbial communities inhabiting plastic-contaminated sediments remain poorly characterized in highly polluted hotspots. In this study, we conducted a genome-resolved metagenomic investigation of sediment sample from plastic pollution hotspot in India. Using Illumina short-read sequencing and three high-performing binning tools we reconstructed 52 non-redundant metagenome-assembled genomes (MAGs) from 2,374 initial bins. All MAGs met the MIMAG criteria with 15% reaching near-complete genomes. Taxonomic classification revealed diverse representation of 18 different phyla. Interestingly, 90% of the MAGs could only be classified at intermediate taxonomic levels in the Genome Taxonomy Database (GTDB), suggesting the presence of novel microbial lineages. Taxonomic novelty was further confirmed using the Type Strain Genome Server (TYGS), which identified 3 novel orders, 16 families, and 28 genera. This study provides the first comprehensive genomic insight into microbial communities from plastic-polluted coastal sediments in India and lays the groundwork for exploring their ecological functions.

As one of the most common and deadly cancers in the world, colorectal cancer (CRC) calls for the creation of innovative treatment approaches. Although bonducellin, a naturally occurring flavonoid produced from Caesalpinia bonducella, has shown encouraging anticancer effects, little is known about its molecular mode of action in colorectal cancer. In this work, we examined Bonducellin's potential as an inhibitor of methyltransferase-like 3 (METTL3), a crucial RNA methyltransferase linked to the development of colorectal cancer. According to molecular docking, Bonducellin has a high affinity for the RNA binding pocket of METTL3, which is maintained by hydrophobic and hydrogen bonding interactions. Bonducellin dramatically reduced the migration, colony formation, and proliferation of SW480 and SW620 cells in in vitro tests, with IC 50 values of 43.5 µM and 51.3 µM. Apoptosis induction was validated by acridine orange/ethidium bromide staining, and the anti-angiogenic function of bonducellin was established by the CAM test. All of these results point to Bonducellin's potential as a natural small-molecule lead for CRC therapy by showing that it inhibits METTL3 and induces apoptosis to limit CRC cell proliferation and migration.

Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC50 values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines.

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.

Colorectal cancer is the third leading cause of cancer worldwide, following lung and breast cancer. With an alarming prevalence, the disease is associated with abnormalities in the JAK2/STAT3 signalling pathway, which contributes to processes such as apoptosis and cell proliferation. This underscores a need for novel therapeutic strategies targeting this pathway to address treatment resistance and enhance efficacy. This study aimed to identify an effective synthetic drug for colon cancer by targeting the JAK2 protein. We employed computational methodologies, including molecular docking, cross-docking, and molecular dynamics (MD) simulations, particularly utilizing the Discovery Diversity Set from the Enamine library. Ten compounds with significantly greater binding affinities to JAK2 than the reference inhibitor fedratinib were selected based on their binding affinities, which ranged from − 11.6 kcal/mol to − 8.6 kcal/mol, coupled with low inhibition constants (0.005–7.590 μM) and favourable intermolecular interactions with key amino acid residues in the ATP-binding site. Cross-docking was performed to confirm selectivity against the JAK2 protein, and c-DFT analyses assessed inhibitor efficacy. The top lead compounds underwent a 300 ns MD analysis, revealing compounds L1 and L3 to exhibit favourable drug-likeness and robust ADMET profiles. The overall computational investigation demonstrated that these lead compounds serve as potent and selective inhibitors of JAK2.

JAK1, a key regulator of multiple oncogenic pathways, is a sought-out target, and its expression in immune cells and tumour-infiltrating lymphocytes (TILs) is associated with a favorable prognosis in breast cancer. JAK1 activates IL-6 via ERBB2 receptor tyrosine kinase signalling and promotes metastatic cancer and STAT3 activation in breast cancer cells. Hence, targeting JAK1 in breast cancer is being explored as a potential therapeutic strategy. A comprehensive in silico approach was utilised in this study to identify selective JAK1 inhibitors from the Life chemicals database. First, we utilised an anticancer focussed library and performed molecular docking to screen against JAK1 protein. The top 10 compounds from docking were taken for cross-docking, to assess the selectivity towards JAK1 target. Lipinski's RO5 was checked for eliminating the compounds that violate rules. Toxicity, biological activity and reactivity for the identified best compounds were predicted by Protox-II server, PASS server and cDFT analysis respectively. MD simulations were carried out to examine the stability and dynamic behaviour of the top leads, including the long-term stability of the ligand-receptor complex and any conformational changes. Lastly, the MM/PBSA method was used to determine the binding free energy of the protein-ligand complex. Our in silico approach has yielded a promising set of compounds F2638-0133, F3408-0020 and F5833-7435 with the potential to selectively target JAK1, a critical player in breast cancer progression. The docking, simulation and MM/PBSA results were compared with standard drug abrocitinib. Identified compounds exhibit favorable binding interactions, electronic properties and robust stability profiles compared to standard drug, making them promising leads for further experimental validation.

Leptospira interrogans, known for its association with environmental biofilms, poses significant challenges in managing leptospirosis due to its persistent virulence and resistance to antimicrobial agents. Addressing the biofilms in infection and resistance necessitates novel anti-leptospiral agents and strategies, with bioactive compounds offering better biomolecules to combat leptospiral biofilms. This study investigates the role of eugenol against L. interrogans, which has been unexplored. In this study, we have evaluated the impact of eugenol on the growth and biofilm formation of L. interrogans. Eugenol inhibited 70% of biofilm formation at its MBIC70 (10 mM). These findings were further validated through fluorescence and scanning electron microscopy to assess cell viability and morphological changes. Furthermore, the expression levels of key genes, csrA and lipL32, associated with bacterial growth and biofilm formation, were analyzed using qRT-PCR. To complement these findings, molecular docking, c-DFT, and ADME profiles were performed to investigate the interaction of eugenol with the transpeptidase/penicillin-binding protein. The results strongly correlate with the biological outcomes observed in the experimental studies, supporting the efficacy of eugenol against L. interrogans.

Prohibitin 1 (PHB1) and Prohibitin 2 (PHB2) play a crucial role in tumorigenesis and are emerging targets for anticancer therapy. This study applied a structure-based drug repurposing approach, screening 35 chemotherapeutic and natural compounds against PHB1 and PHB2 through molecular docking. Six top candidates Ibrutinib, Daunorubicin, Doxorubicin, Rutin, Flavopiridol, and Camptothecin exhibited binding affinities ranging from -9.7 to -10.5 kcal/mol. ADMET profiling identified Ibrutinib and Flavopiridol as drug-like with favourable pharmacokinetics, whereas Rutin violated Lipinski's rules. Molecular dynamics simulations over 200 ns revealed high stability of Ibrutinib-PHB complexes, supported by low RMSD values (∼1.3-2.2 Å). MM/PBSA free energy calculations confirmed Ibrutinib as the strongest binder (-92.1 and -71.6 kJ/mol for PHB1 and PHB2). These findings position Ibrutinib as a promising PHB-targeting agent for cancer therapy. Further in vitro and in vivo validations are essential to confirm its therapeutic potential.

Several anti-inflammatory small molecules have been discovered and utilized in the treatment of various inflammatory and autoimmune diseases. Despite experimental identification of numerous anti-inflammatory peptides, the development of peptide-based drugs remains expensive, time-consuming, and labor-intensive. Small molecules offer higher stability compared to peptides, owing to their chemical synthesis or natural sources. Consequently, there is an urgent need to develop advanced machine learning (ML) methods utilizing a large-scale dataset consisting of experimentally acquired small molecule to enhance precision and efficiency. This study introduces a predictive ML-method, named InFlamPred (Anti-inflammatory Small Molecule Predictor), tailored for anti-inflammatory small molecules. The proposed ML classifier facilitates compound screening in inflammatory diseases. We trained five different ML classifiers—RF, KNN, LGBM, DT, and XGB achieving an overall accuracy ranging from 61 to 75

Background/Objectives: Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). Methods: In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with IC50 values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. Results: A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. Conclusions: This study provides an effective method for screening AISMs, potentially impacting drug discovery and design.

Rheumatoid Arthritis (RA) is a persistent autoimmune disease affecting approximately 0.5-1 percent of the world population. RA prevalence is higher in woman aged between 35 to 50 years than in age matched men, though this difference is less evident among elderly patients. The profound immune specific effects of disrupted JAK3 (Janus kinase 3) signaling highlight the possibility of therapeutic targeting of JAK3 as a highly specific mode of immune system suppression. To address the above problem which is unendurable to patients and in the hope to cater some respite to such suffering we have targeted JAK3 protein and JAK/STAT signaling pathway with compounds downloaded from FDA database, and performed screening of all available compounds docked against JAK3 protein. The difference between the target protein and other proteins of the same family was studied using cross docking and the compounds having higher binding affinity to JAK3 protein also showed more selectivity towards the particular protein. Density functional theory and molecular dynamics simulation study was done to study the compounds at their atomic level to know more about their drug likeliness. At the end of the study and based on our analysis we have come up with three FDA approved drugs that can be proposed as a treatment option for Rheumatoid Arthritis.

The study aims to elucidate the pharmacological mechanism of Rauvolfia tetraphylla against breast cancer through a comprehensive, multi-faceted approach. This includes molecular docking, molecular dynamics, and experimental validation. Initial screening via ADME analysis and network pharmacology identified key compounds and potential targets. Protein-protein interaction (PPI) network analysis pinpointed Yes-associated protein-1 (YAP) as a crucial target. Molecular docking revealed that three compounds-ajmaline, reserpine, and serpentine-exhibited strong binding affinities with YAP, with scores of -6.5 to -6.7 kcal/mol. Molecular dynamics simulations were conducted to assess the stability of these interactions further. Experimental validation showed R. tetraphylla inhibited breast cancer cell proliferation, with an IC50 of 348.69 μg/mL, while demonstrating cytoprotective effects on Vero cells (IC50: 1056.23 μg/mL). Migration assays indicated an 88.5% reduction in cell migration, and increased ROS levels signaled elevated stress in cancer cells. Apoptosis was confirmed by AO/EtBr staining. In vivo validation in a DMBA-induced mouse model confirmed significant tumor growth inhibition, supported by changes in YAP expression and histopathological analysis. These findings highlight R. tetraphylla as a promising therapeutic candidate against breast cancer, offering insights into its mechanisms and potential for future drug development and clinical applications.

Antimicrobial resistance (AMR) is a global issue due to improper drug use in humans and animals. Antimicrobial peptides (AMPs) show promise in targeting bacteria with minimal harm to host cells and low risk of resistance development. Machine learning enhances accuracy in predicting AMPs. Common classifiers include SVM, RF, ANN, LGBM, and DT. This review compares peptide prediction tools based on machine learning, assessing performance using cross-validation. Carefully chosen independent datasets were used to evaluate predictive efficiency. By utilizing a variety of ML methods, the best techniques for predicting Antimicrobial peptides, Antibacterial peptides, Antifungal peptides can be developed quickly

Allergy is the sudden response of the immunity system which arises later the disclosure of allergens like chemicals, proteins and peptides. In the early times, multiple techniques were formed for allergenicity forecasting of peptides and proteins. However, there is no technique to anticipate the chemical and potential allergenic. The standard food allergen recognition mainly depends on vitro and vivo research, which frequently needs more time and is expensive. Artificial Intelligence (AI) operates a quick food allergen detection model, which solves the above-defined problems and becomes an effective auxiliary tool. Evaluation of potential allergenicity of protein is required at any movement of transgenic proteins implemented in the food chain. Therefore, it is necessary to solve more complications involved in the conventional allergenicity prediction technique for chemical compounds. Thus, a new allergenicity prediction framework for chemical compounds is designed using deep learning techniques. Initially, essential allergenicity data are accumulated from the standard measures. Next, the acquired data are presented for the optimal weighted feature selection stage. In this phase, the weights and features are selected optimally using Opposition Tasmanian Devil Optimization (OTDO). Further, the adopted optimal weighted features are offered to the allergenicity forecasting stage. Here, the allergenicity is predicated on using the developed "Dilated Deep Temporal Context Networks with Residual Long Short-Term Memory Networks (DiDNet-ResLSTM)". Hence, the recommended allergenicity prediction model effectively higher performance rate in different experimental observations.

Plastic accumulation has become a serious environmental threat. Mitigation of plastic is important to save the ecosystem of our planet. With current research being focused on microbial degradation of plastics, microbes with the potential to degrade polyethylene were isolated in this study. In vitro studies were performed to define the correlation between the degrading capability of the isolates and laccase, a common oxidase enzyme. Instrumental analyses were used to evaluate morphological and chemical modifications in polyethylene, which demonstrated a steady onset of the degradation process in case of both isolates, Pseudomonas aeruginosa O1-P and Bacillus cereus O2-B. To understand the efficiency of laccase in degrading other common polymers, in silico approach was employed, for which 3D structures of laccase in both the isolates were constructed via homology modeling and molecular docking was performed, revealing that the enzyme laccase can be exploited to degrade a wide range of polymers.