ArticleScientific reports2025
A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- On machine learning based QSPR analysis of amphetamine derivatives using regression models.Scientific reports · 2026Article
- Multi criterion decision making analysis of hematologic cancer drugs via topological indices and physicochemical properties.Scientific reports · 2025Article
- Predicting bone cancer drugs properties through topological indices and machine learning.Scientific reports · 2025Article
- Evaluation of antiarrhythmia drug through QSPR modeling and multi criteria decision analysis.Scientific reports · 2025Article
- Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics.Scientific reports · 2025Article
- Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis.Scientific reports · 2025Article
- A graph-based computational approach for modeling physicochemical properties in drug design.Scientific reports · 2025Article
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6 authors.
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Abstract
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ([Formula: see text] to [Formula: see text]). We evaluated the models based on performance metrics such as R and [Formula: see text] and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and [Formula: see text].
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