ReviewInternational journal of molecular sciences2009
Current mathematical methods used in QSAR/QSPR studies.
Review in International journal of molecular sciences, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Hybrid APSO-Bayesian optimized triple-kernel SVR models for QSAR and rational design of 1H-pyrrolo[2,3-c]pyridine-based LSD1 Inhibitors.Molecular diversity · 2026Article
- Unveiling the electronic and thermodynamic properties of 2-furaldehyde semicarbazone derivatives: combined DFT and M-polynomial topological indices analysis.Scientific reports · 2026Article
- QSAR-Guided Design of Serotonin Transporter Inhibitors Supported by Molecular Docking and Biased Molecular Dynamics.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Duality of Simplicity and Accuracy in QSPR: A Machine Learning Framework for Predicting Solubility of Selected Pharmaceutical Acids in Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2025Article
- Predicting flavonoid physicochemical properties using topological indices and regression modeling.Scientific reports · 2025Article
- Comparative QSPR study of food preservatives using topological indices and regression models.Scientific reports · 2025Article
- Physicochemical profiling and ranking of parkinson's disease drugs through QSPR and Fuzzy TOPSIS analysis.Scientific reports · 2025Article
- Predictive analysis of vitiligo treatment drugs using degree and neighborhood degree-based topological descriptors.Scientific reports · 2025Article
- Advances in the Development of Mitochondrial Pyruvate Carrier Inhibitors for Therapeutic Applications.Biomolecules · 2025Review
- Multi-method computational evaluation of the inhibitors against leucine-rich repeat kinase 2 G2019S mutant for Parkinson's disease.Molecular diversity · 2024Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors.Molecular diversity · 2024Article
- Curcumin-Conjugated PAMAM Dendrimers of Two Generations: Comparative Analysis of Physiochemical Properties Using Adriatic Topological Indices.ACS omega · 2024Article
- Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability.Pharmaceutical research · 2024Article
- Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learningFrontiers in chemistry · 2024Article
- Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.Current topics in medicinal chemistry · 2024Review
- Steroids Bearing Heteroatom as Potential Drugs for Medicine.Biomedicines · 2023Review
- Structure-Activity Relationship Studies of Chalcones and Diarylpentanoids with Antitumor Activity: Potency and Selectivity Optimization.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds.International journal of molecular sciences · 2022Article
- Antioxidant Properties of Hemp Proteins: From Functional Food to Phytotherapy and Beyond.Molecules (Basel, Switzerland) · 2022Review
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Authors and funding
2 authors.
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Abstract
This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.
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