ArticleJournal of chemical information and modeling
A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.
Article in Journal of chemical information and modeling. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.Molecular diversity · 2024Article
- Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.Molecular diversity · 2024Article
- Enhanced Deep-Learning Model for Carbon Footprints of Chemicals.ACS sustainable chemistry & engineering · 2024Article
- Development and evaluation of a java-based deep neural network method for drug response predictions.Frontiers in artificial intelligence · 2023Article
- Machine Learning Study of Metabolic NetworksMolecular pharmaceutics · 2022Article
- Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier.Journal of agricultural and food chemistry · 2022Article
- A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening.International journal of molecular sciences · 2021Article
- Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach.Biomedicines · 2021Article
- Article
- QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids.Molecules (Basel, Switzerland) · 2020Article
- Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.Nanomaterials (Basel, Switzerland) · 2020Article
- Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).Molecular pharmaceutics · 2020Article
- In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method.International journal of molecular sciences · 2019Article
- Larvicidal Compounds Extracted fromMolecules (Basel, Switzerland) · 2019Article
- Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models.International journal of molecular sciences · 2019Article
- Article
- A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform.Nanoscale advances · 2019Article
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5 authors.
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Abstract
A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.
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