ArticleMethods in molecular biology (Clifton, N.J.)2012
Predicting the biological activities through QSAR analysis and docking-based scoring.
Article in Methods in molecular biology (Clifton, N.J.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Virtual Screening of FDA-Approved Compounds: Exploring New Alternatives for HIV Treatment.ACS omega · 2026Article
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- Synthesis of Novel Tricyclic N-Acylhydrazones as Tubulin Polymerization Inhibitors.International journal of molecular sciences · 2025Article
- Potential Benefits of In Silico Methods: A Promising Alternative in Natural Compound's Drug Discovery and Repurposing for HBV Therapy.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Importance of Computer-aided Drug Design in Modern Pharmaceutical Research.Current drug discovery technologies · 2025Review
- Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software.NPP - digital psychiatry and neuroscience · 2025Review
- Harnessing Drug Repurposing to Combat Breast Cancer by Targeting Altered Metabolism and Epithelial-to-Mesenchymal Transition Pathways.ACS pharmacology & translational science · 2024Review
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- Searching for Protein Off-Targets of Prostate-Specific Membrane Antigen-Targeting Radioligands in the Salivary Glands.Cancer biotherapy & radiopharmaceuticals · 2024Article
- A Chronicle Review ofIndian journal of microbiology · 2024Review
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
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- AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability.Frontiers in toxicology · 2024Review
- QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.Molecules (Basel, Switzerland) · 2022Article
- Interrogation ofSaudi journal of biological sciences · 2021Article
- Anti-SARS-CoV Natural Products With the Potential to Inhibit SARS-CoV-2 (COVID-19).Frontiers in pharmacology · 2020Review
- Computational studies to predict or explain G protein coupled receptor polypharmacology.Trends in pharmacological sciences · 2014Article
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2 authors.
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Abstract
Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.
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