Computational Identification of High-Affinity Kinase Inhibitors through Molecular Representation Learning and Drug–Target Interaction Modeling
The identification of high-affinity kinase inhibitors is a key objective in computational drug discovery because protein kinases regulate numerous biological processes associated with cancer and other complex diseases. This study developed a molecular representation learning framework integrated with drug–target interaction modeling to predict binding affinity between small-molecule inhibitors and kinase proteins. Experimentally validated interaction data from the KIBA and Davis benchmark datasets were used for model development and external validation. Drug molecules represented by canonical SMILES strings and kinase protein sequences were transformed into informative numerical representations to enable supervised affinity prediction. The proposed framework demonstrated satisfactory predictive performance, with superior accuracy achieved on the KIBA dataset while maintaining good generalizability on the independent Davis dataset. High-affinity drug–target interactions were successfully prioritized, indicating that representation-based learning effectively captured the structural patterns governing kinase–inhibitor binding. The findings demonstrate that molecular representation learning offers a reliable and scalable strategy for computational identification of kinase inhibitors while reducing dependence on extensive experimental screening. This framework can support early-stage drug discovery by improving affinity prediction and facilitating the prioritization of promising therapeutic candidates for subsequent experimental validation.
Copyright (c) 2026 European Journal of Molecular Pharmacy
Machine Learning-Based Prediction of Drug–Target Binding Affinity Using Molecular Structures and Protein Sequences: A Comparative Analysis of Davis and KIBA Datasets
Determination of the drug–target binding affinity (DTA) is a critical part of the drug discovery process, allowing for the fast selection of therapeutic candidates with high binding affinity to a target, thus minimizing the reliance on expensive experimental screening. This paper proposed and evaluated three machine learning algorithms (Random Forest, XGBoost, and LightGBM) for the prediction of binding affinity based on molecular structure and protein sequences from the Davis and KIBA benchmark datasets. Molecular fingerprints and physicochemical descriptors of drug molecules were used, while amino acid composition and sequence-derived features of protein targets were used. The data was preprocessed, features engineered, and models trained and tested using standard regression assessment measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), coefficient of determination (R²), Pearson correlation coefficient, and Concordance Index. We showed that the method (LightGBM or Random Forest) that could predict best is dependent on the dataset, as LightGBM outperformed Random Forest on the Davis dataset and the opposite was true for the KIBA dataset, showing that algorithm performance is dependent on the dataset and interaction complexity. Further, feature importance analysis showed that molecular descriptors as well as protein sequence derived features were both important in the prediction accuracy. In general, the proposed framework is a reliable, repeatable and computationally inexpensive method for predicting a drug-target affinity that can be used for virtual screening and prioritizing leads in the early drug discovery phase. The results also provide insights into which models are suitable for which benchmark dataset based on its characteristics.
AI-Integrated Drug–Target Interaction Prediction for High-Affinity Kinase Inhibitors Using Molecular Modeling and Virtual Screening
In the field of drug discovery, artificial intelligence (AI) has proven to be a gamechanger by enhancing the prediction of drug–target interaction (DTI) and accelerating discovery of potential therapeutic candidates. This research aimed to build an AI-based computational system to model and virtually screen drugs to predict drug–target interaction and discover high affinity kinase inhibitors. To perform the in-silico analysis, a secondary dataset of 1,000 drug–target interaction records was created, validated for the presence of both compound SMILES and protein target sequences, and binding affinity values were obtained from experiments. Following data preprocessing, molecular descriptors and protein sequence features were utilized to develop an AI-based regression model for binding affinity prediction. The compounds were then prioritized using a virtual screening workflow with the predicted affinity values. Molecular diversity of curated dataset consisted of 479 unique compounds and 326 protein targets in the range of 5.00 to 15.20. A significant percentage of the interactions had high binding affinity which enabled good model development and prioritization of candidates. The suggested framework was effective in pinpointing compounds with improved predicted affinity, underscoring the potential of AI-driven computational strategies in speeding up the process of identifying compounds that target kinases. In sum, the combination of AI and virtual screening with molecular modeling offers a robust method for rational lead identification and lays the groundwork for computational studies in the future for structure validation and experimental exploration.
Artificial Intelligence in Molecular Drug Discovery: Advances in Computational Pharmacology, Rational Drug Design, and Translational Therapeutics
Artificial intelligence is increasingly transforming molecular drug discovery by enabling data-driven target identification, molecular representation, structure prediction, virtual screening, de novo design, lead optimization, pharmacokinetic modelling, toxicity prediction, drug repurposing, and preclinical translation. This review examines the methodological foundations and therapeutic applications of machine learning, deep learning, graph neural networks, generative models, network pharmacology, quantitative structure–activity relationship modelling, and multimodal computational platforms across the drug-development continuum. AI systems can integrate chemical, structural, multi-omics, pharmacological, and clinical data to prioritize disease-relevant targets, explore ultra-large chemical spaces, predict protein–ligand interactions, optimize candidate properties, and identify efficacy or safety liabilities earlier. Their application may reduce experimental burden, improve decision-making, and support precision therapeutics by linking molecular characteristics with patient-specific response patterns. Translation remains constrained by incomplete and biased datasets, inconsistent assay standards, weak external validation, limited interpretability, uncertain causal relevance, and poor generalizability across populations and disease settings. Computational predictions cannot replace experimental pharmacology, medicinal chemistry, human-relevant models, or clinical evaluation. Future progress requires explainable and uncertainty-aware models, standardized benchmarks, prospective validation, federated data infrastructures, multimodal foundation models, and closed-loop integration with automated synthesis and biological testing. Rigorous governance and multidisciplinary collaboration will determine whether AI delivers safer, more efficient, and clinically meaningful therapeutic development for diverse diseases and unmet medical needs worldwide.
Nanotechnology-Enabled Drug Delivery Systems: Molecular Design, Therapeutic Applications, and Future Perspectives
Nanotechnology-enabled drug delivery systems offer new ways to improve the solubility, stability, targeting, release, and intracellular transport of therapeutic agents. This review examines how molecular composition, particle size, morphology, surface charge, rigidity, drug–carrier interactions, and surface functionalisation shape nanocarrier behaviour across biological environments. Major lipid-based, polymeric, inorganic, biological, biomimetic, and hybrid platforms are compared in relation to payload compatibility, pharmacokinetics, pharmacodynamics, and therapeutic performance. The review also evaluates passive and active targeting, biological barrier navigation, organelle-specific delivery, and stimuli-responsive release. Applications are discussed across cancer, infectious disease, vaccination, neurological disorders, cardiovascular and metabolic conditions, inflammatory and autoimmune diseases, and ocular, pulmonary, and transdermal therapy. Particular attention is given to the delivery of proteins, peptides, messenger RNA, small interfering RNA, microRNA, monoclonal antibodies, and genome-editing components. Despite major advances, clinical translation remains limited by variable protein corona formation, immune responses, toxicity, formulation instability, manufacturing complexity, inconsistent scale-up, and regulatory uncertainty. Future progress will depend on standardised evaluation, predictive human-relevant models, biomarker-guided treatment, artificial intelligence-assisted design, biodegradable materials, and continuous manufacturing. Integrating molecular engineering with pharmacological performance, safety, clinical need, and industrial feasibility is essential for converting advanced nanocarriers into reliable and accessible pharmaceutical products with measurable therapeutic advantages over conventional formulations and clear benefits for diverse patient populations in clinical practice.
ALDH1A1 Expression in Colorectal Carcinoma: Immunohistochemical Evaluation and Correlation with Pathological Prognostic Factors
Background: Colorectal cancer (CRC) is among the most prevalent cancers worldwide, marked by substantial biological variability. Aldehyde dehydrogenase 1A1 (ALDH1A1), which is a known cancer stem cell biomarker, has shown promise as a biomarker of aggressive cancer, although there is very little research done in South Asian populations. Objectives: To study the IHC staining of ALDH1A1 in surgically removed CRC samples and its association with various pathological prognostic markers such as tumor grade, lymphovascular invasion (LVI), lymph node involvement, and AJCC pathological stage. Materials and Methods: A prospective cross-sectional study was conducted over two years (March 2024–February 2026) at a tertiary academic centre. Fifty-five consecutively enrolled radical resection specimens of histopathologically confirmed CRC were assessed. ALDH1A1 IHC was performed using a primary mouse monoclonal antibody with heat-induced antigen retrieval. Expression was scored semi-quantitatively using a proportion score (0–3), intensity score (0–3), and composite H-score (0–300). Cases were classified as high (combined score 2–3) or low expression (combined score 0–1). Associations with clinicopathological variables were assessed using Fisher's exact test; receiver operating characteristic (ROC) examination was performed to determine the optimal H-score threshold. Results: High ALDH1A1 expression was noticed in 43 of 55 cases (78.2%), with a mean H-score of 146.71 ± 82.01. Statistically significant associations were identified between high ALDH1A1 expression and lymphovascular invasion (90.3% vs. 62.5% in LVI-negative; p = 0.013), lymph node metastasis (86.2% vs. 69.2% in node-negative; p = 0.031), and advanced AJCC stage (p = 0.002). No significant association was observed with tumour site (p = 0.434) or tumour grade (p = 0.338), although poorly differentiated tumours showed the highest proportion of high expression (89.5%). ROC analysis demonstrated excellent discriminatory performance for H-score (AUC = 0.963; 95% CI 0.893–1.000; sensitivity 95.2%; specificity 92.3%; optimal cut-off ≥ 97.5). Conclusion: Increased expression of ALDH1A1 represents an often observed and biologically relevant phenomenon in CRC that shows strong associations with markers of invasive/metastatic behavior. The H-score system for the immunohistochemical evaluation allows a reliable distinction of different expression patterns and could be included into the standard histopathological report.
Therapeutic Enzymes in Modern Medicine: Biocatalytic Mechanisms, Clinical Applications, Engineering and Challenges
The mortal genome has around 25,000 to 30,000 genes, but these genes can produce numerous further proteins through a process known as indispensable splicing. A lot of these proteins are enzymes that help control different processes inside cells and in the body. However, it can change how cells bear, which may beget complaint, If these enzymes do not work duly. That is why enzymes are important targets for making both natural and man- made medicines that can control their exertion. The different jobs enzymes do show how important they're in the body and can give important information about someone's health. Detecting dangerous agents, especially contagions, is frequently done by checking for certain enzyme conditioning. Enzymes are also good because they're veritably specific, do not generally beget the body to reject them, are stable, and work constantly, making them great options for use in treatments. improvements in recombinant technology and protein engineering have led to the creation of enzymes that are used in numerous areas, similar as diagnosing conditions and developing drugs using enzyme- grounded tests and Eco-friendly chemistry approaches. Overall, enzymes are veritably important in healthcare and complaint discovery. In this chapter, we’ll look at well- known enzymes used in drug, fastening on new treatment pretensions and advanced styles being used as new ways to treat conditions that were formerly untreatable. Enzymes are crucial in ultramodern drug, acting as essential catalysts in both treatments and tests. This composition covers their part in enzyme relief remedy, creating new medicines, and clinical exploration. Advances in biotechnology have helped make enzymes more stable and effective, working issues like breakdown, vulnerable responses, and high costs to produce them. Recent developments, like enzyme immobilization, delivery systems using nanotechnology, and enzymes made with CRISPR technology, have greatly increased how enzymes are used in treatments. Indeed, though there are still some challenges, ongoing exploration is making enzyme curatives more available and effective. This study shows how important enzymes have changed drug and points out where unborn advancements could take them for better use in clinical settings.
Rarity Redefined: Anti-Ku Antibody–Associated Myopathy In a Child
Background Anti-Ku antibody–associated inflammatory myopathy is a rare autoimmune disorder, predominantly described in adults and only rarely reported in pediatric patients. Case-Presentation An 8-year-old male presented with a one-month history of high-grade fever followed by progressive proximal muscle weakness, leading to difficulty in walking and performing overhead activities. There was no history of rash, joint symptoms, or trauma. Examination revealed symmetric proximal muscle weakness. Management & Outcome Laboratory evaluation showed markedly elevated muscle enzymes (CK, AST, LDH) and inflammatory markers. Autoimmune workup revealed positivity for anti-Ku antibodies. MRI of lower limbs demonstrated T2/STIR hyperintensities suggestive of inflammatory myopathy. The patient was treated with pulse methylprednisolone followed by oral corticosteroids and methotrexate, resulting in significant clinical improvement and restoration of functional mobility. Conclusion This case highlights a rare early presentation of anti-Ku myopathy in childhood. Early recognition using serological and radiological correlation and prompt immunosuppressive therapy can significantly improve outcomes.
Semaglutide as a Renoprotective Metabolic Modulator in Diabetic Nephropathy: An Expanded Case Series from West Bengal, India
Diabetic nephropathy remains one of the most significant etiological drivers of chronic kidney disease and end-stage renal failure worldwide, with a particularly rising burden in developing regions such as India. Semaglutide, a glucagon-like peptide-1 receptor agonist, has recently emerged as a multifunctional therapeutic agent exhibiting glycemic, cardiovascular, and potential renoprotective effects beyond conventional metabolic control. This case series evaluates the effect of semaglutide on urine albumin-creatinine ratio (UACR) and glycemic indices in non- obese patients with diabetic nephropathy from West Bengal, India. Eighteen patients were observed over a study period from November 2025 to March 202C, receiving semaglutide 0.5 mg subcutaneously once weekly for three months as an adjunct to standard therapy. Comparative evaluation of baseline and follow-up parameters demonstrated a consistent reduction in albuminuria, with mean UACR declining from 418 mg/g to 252 mg/g, representing an approximate 3U.7% reduction. Mean HbA1c improved from 8.72% to 7.18% over the same period. These findings reinforce the potential role of semaglutide as a reno-metabolic modulator in diabetic kidney disease.
Urinary Free Cortisol Assessment for the Detection of Occult Hypercortisolism in Type 2 Diabetes Mellitus with Difficult-to-Control Hypertension: Clinical Insights from a Prospective Case Series
Background Type 2 diabetes mellitus (T2DM) and hypertension commonly coexist and significantly contribute to cardiovascular and renal morbidity. In a subset of patients, difficult-to-control hypertension may be driven by secondary endocrine abnormalities, including hypercortisolism. Urinary free cortisol (UFC) remains an established screening tool for the evaluation of endogenous cortisol excess. Objectives To evaluate the utility of 24-hour urinary free cortisol assessment in detecting occult hypercortisolism among patients with T2DM and difficult-to-control hypertension attending a tertiary care centre in Kolkata. Methods This prospective case series was conducted between August 2025 and April 202G. Twenty-nine consecutive adults with T2DM and difficult-to-control hypertension underwent 24-hour UFC estimation as part of endocrine evaluation. Clinical, biochemical and metabolic parameters were assessed and analysed. Results Elevated UFC levels were observed in G of 2U patients (20.7%). Persistent biochemical evidence of cortisol excess was identified in three patients (10.3%), including two cases of adrenal adenoma-associated hypercortisolism and one case of mild autonomous cortisol secretion. Patients with elevated UFC levels demonstrated poorer glycaemic control, greater obesity and a higher prevalence of albuminuria. Conclusion UFC testing identified clinically relevant cortisol excess in a subset of diabetic patients with difficult-to-control hypertension. Selective screening in high-risk individuals may facilitate earlier diagnosis of occult hypercortisolism and enable targeted therapeutic intervention.
Polycystic Ovary Syndrome and the Imperative for Personalized Dosing: Bridging the Gap Between Phenotypic Heterogeneity and Individualized Pharmacotherapy
Polycystic ovary syndrome (PCOS) is a prevalent, complex endocrinopathy associated with reproductive, metabolic and psychological manifestations, and available treatment is still largely empirical and “one size fits all”. This is a narrative review of the pathophysiological and clinical spectrum of PCOS and current therapeutic approaches with a personalized dosing approach. The Rotterdam criteria (A-D) describe and illustrate a very broad spectrum of phenotypes in women with PCOS that are characterized by different degrees of androgen excess, ovulatory dysfunction, and metabolic risk, but current guidelines for treatment for metformin, ovulation induction agents, combined oral contraceptive, anti-androgens, inositols, and emerging GLP 1 receptor agonists or SGLT2 inhibitors do not cover this spectrum. There are multiple validated biomarkers that contribute to disease expression and drug response (such as HOMA IR, anti Müllerian hormone, total testosterone, sex hormone binding globulin, free androgen index) but do not form part of the dose selection or dose titration protocols. The lack of phenotype stratified dose-finding trials, no biomarker-guided titration algorithms, low level of attention to ethnic and anthropometric considerations and no clinically used AI tools to assist with dose optimisation are all critical gaps across domains. These gaps will be addressed with a set of four pillars: a phenotypic drug selection, biomarker-based dose titration, pharmacogenomic profiling in treatment resistant cases, and the creation of AI-supported clinical decision tools. The use of adequately powered PCOS phenotype-stratified randomised trials and real-world, inclusive datasets, within the framework of this strategy, represents a realistic way to move towards precision-based care and minimise treatment failure, side effects and long term cardiometabolic risk in the large PCOS patient population.
Integrated Pharmacokinetic and Drug–Drug Interaction Analysis Linking Exposure Variability to Clinical Outcomes
efficacy and safety, particularly in complex pharmacotherapy involving multiple agents. The present study aimed to develop an integrated analytical framework linking pharmacokinetics, interaction mechanisms, and clinical safety outcomes to support therapeutic optimization. A comprehensive multi-domain dataset encompassing pharmacokinetic parameters, drug–drug interaction profiles, toxicity records, and adverse event data was systematically analyzed. Pharmacokinetic variability was assessed using key exposure metrics, including maximum plasma concentration, area under the curve, and elimination half-life. Interaction patterns were evaluated through enzyme- and transporter-mediated mechanisms, with particular emphasis on cytochrome P450 systems. Integrated analyses revealed that compounds with higher interaction burden exhibited increased variability in exposure parameters and a greater frequency of adverse events. Exposure–toxicity relationships demonstrated that elevated systemic exposure was associated with increased safety risk. Furthermore, discontinuation-related adverse outcomes were identified as the most clinically relevant indicators of therapeutic failure.These findings highlight the importance of integrating pharmacokinetic and interaction data to improve prediction of clinical outcomes. The study provides a mechanistic and translational framework for optimizing dosing strategies, minimizing adverse effects, and enhancing therapeutic effectiveness in clinical practice.
Computational Pharmacology and Toxicity Modeling Datasets for Drug Discovery and Translational Research
The integration of computational approaches with pharmacological data has transformed modern drug discovery, enabling efficient prediction of molecular properties, toxicity, and clinical outcomes. This study presents a unified framework that leverages multiple publicly available datasets, including SIDER, ClinTox, Tox21, ToxCast, and BBBP, to model drug behavior across molecular, biological, and translational levels. Comprehensive data preprocessing, feature engineering, and machine learning techniques were applied to capture structure–activity relationships and predict pharmacological endpoints. A combination of classical machine learning and deep learning models, including graph-based architectures and multi-task learning frameworks, was employed to address dataset heterogeneity and improve predictive performance. The results demonstrate strong predictive capability for toxicity and permeability endpoints, with improved generalization achieved through scaffold-based data splitting and ensemble modeling. Feature importance analysis identified key physicochemical properties influencing drug response, while integration across datasets enabled the construction of comprehensive pharmacological profiles. The study highlights the value of combining mechanistic bioassay data with clinical outcome datasets to bridge the gap between molecular-level predictions and real-world therapeutic relevance. Limitations related to data imbalance and lack of formulation-specific information were identified, indicating areas for future improvement. Overall, the proposed framework supports efficient and scalable drug discovery by enhancing predictive accuracy and enabling informed decision-making in early-stage development.
Disproportionality Analysis of Drug–Drug Interactions: A Pharmacovigilance Approach
Drug–drug interactions (DDIs) represent a significant concern in clinical practice, particularly in the context of polypharmacy and complex therapeutic regimens. This study aimed to identify and evaluate potential DDI signals using a pharmacovigilance-based disproportionality approach applied to the FDA Adverse Event Reporting System (FAERS) database. Data from the FAERS 2025 Q4 dataset were systematically processed, including deduplication, drug standardization, and identification of co-reported suspect drug pairs. Disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), and associated confidence intervals to detect statistically significant signals. A total of 66,947 reports met the inclusion criteria, yielding 226,169 unique drug pairs for analysis. Several high-frequency and high-signal drug combinations were identified, many of which were associated with clinically relevant adverse events consistent with known pharmacological mechanisms. The application of multiple statistical measures demonstrated strong concordance, supporting the robustness of the findings. Sensitivity and subgroup analyses further confirmed the stability of detected signals across different analytical conditions. The results highlight the utility of FAERS for large-scale DDI signal detection and underscore the importance of integrating pharmacovigilance data with pharmacokinetic and pharmacodynamic insights. While limitations such as reporting bias and lack of causal inference remain, this approach provides valuable real-world evidence for hypothesis generation and risk assessment. These findings support enhanced drug safety monitoring and inform clinical decision-making.
Development and Validation of A Hyphenated Method for The Determination of Dapagliflozin and Bisoprolol
The simultaneous quantification of antidiabetic and cardiovascular drugs is essential for managing comorbid conditions where polypharmacy is prevalent. Dapagliflozin, a sodium-glucose co-transporter 2 inhibitor, and Bisoprolol, a β1-selective adrenergic blocker, were determined in pharmaceutical dosage forms using a unique LC-MS/MS approach that was developed and validated in this study. Method development was guided by physicochemical properties, utilizing a C18 column (250 × 4.6 mm, 5 µm) with a methanol–phosphate buffer (60:40, pH 3.8) mobile phase. Chromatographic separation was achieved within 5 minutes, with retention times of 2.7 and 3.4 minutes, respectively, ensuring high analytical throughput and efficient resolution. Mass spectrometric finding using electrospray ionization in positive mode with optimized MRM transitions (m/z 408.3 → 135.2 and 326.2 → 116.1) provided high sensitivity and selectivity. The method demonstrated excellent linearity (5-100 ng/mL) with correlation coefficients of 0.998 and 0.999. Low LOD (~1 ng/mL) and LOQ (~3 ng/mL) values confirmed its sensitivity. Validation in accordance with ICH Q2(R2) guidelines showed accuracy (98-102%), precision (%RSD <2%), robustness, and specificity. The developed method is rapid, reliable, and suitable for routine quality control. Furthermore, its high sensitivity supports bioanalytical applications such as pharmacokinetic profiling and therapeutic drug monitoring. The proposed hyphenated analytical platform represents a significant advancement in pharmaceutical biotechnology, enabling scalable, high-throughput analysis for modern drug development, precision therapeutics, and translational research.
Pharmacogenomics and Personalized Medicine: A Comprehensive Review of Clinical Implementation, Genetic Determinants, and Future Frontiers
Pharmacogenomics (PGx), the convergence of pharmacology and genomics, has evolved from a theoretical discipline into a cornerstone of precision medicine. By elucidating the genetic basis for interindividual variability in drug response, PGx aims to maximize therapeutic efficacy while minimizing adverse drug reactions (ADRs). This comprehensive research paper provides an exhaustive analysis of the current state of pharmacogenomics as of late 2025. It synthesizes data regarding key drug-gene interactions—specifically involving CYP2C19, CYP2D6, CYP2C9, VKORC1, DPYD, HLA-B, and SLCO1B1—and examines the evolution of clinical guidelines from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and other regulatory bodies. Furthermore, this report critically evaluates the integration of Artificial Intelligence (AI) in predictive toxicology and dosing algorithms, assesses the barriers to clinical implementation, and provides a forward-looking perspective on the field's trajectory. The analysis highlights pivotal 2025 updates regarding statin safety and SLCO1B1 allele reclassification, marking a significant refinement in cardiovascular precision medicine. The review also incorporates data from recent pilot implementation studies and regulatory updates from the FDA and NCCN concerning fluoropyrimidine toxicity.