Smart Simulations for Scarred Livers: Target-Based Computational Models in Cirrhosis Care

Recent advances in computational drug discovery, including Quantitative Structure-Activity Relationship (QSAR) modelling, molecular docking and molecular dynamics, have paved the way for identifying novel therapeutic candidates targeting key pathways involved in fibrosis and chronic inflammation. The present research retrieved the data from ChEMBL database to develop the binary classification-based machine learning models for four critical targets: Transforming Growth Factor Beta 1 (TGF-β1), Platelet-Derived Growth Factor (PDGF), Inhibitor of Nuclear Factor Kappa-B Kinase Subunit Beta (IKKB), and Tumor Necrosis Factor Alpha (TNF-α). These targets are implicated in diseases such as liver fibrosis, non-alcoholic steatohepatitis (NASH), and Cirrhosis. We discuss the pharmacotherapeutic relevance of predicted active compounds, their mechanisms of action, and implications for clinical pharmacy practice, including drug monitoring and therapy optimization.The study demonstrates how clinical pharmacists may navigate experimental discoveries with practical therapeutic approaches by using computational predictions to inform drug selection, repurposing, and customized patient care.The four machine learning models, from each selected through comparative analysis between 11 classifiers were rigorously validated with 10x10 K-fold cross validation and achieves MCC_ext ≥ 0.54 and Accuracy_ext ≥ 0.74 indicating robust predictive ability. Target based molecular simulations were conducted to chosen pairs of predicted actives and inactives in each target to further validate the discriminatory potential of the model from a simulation point of view and was found to be consistent with the binding scores and dynamics .Furthermore, these models were incorporated into a single web platform with defined pairwise tanimoto similarity-based applicability domain and hosted in the provided link: https://mutli-model-cirrhosis.streamlit.app/ allowing researchers to utilize the predictive models effectively. Moreover, early identification of potential responders through such computational tools may reduce trial-and-error prescribing, minimize adverse effects, and improve overall treatment outcomes in patients with chronic liver diseases.