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Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
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Evolution of PLS for Modeling SAR and omics Data
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QSAR of Antiproliferative Activity of N‐Substituted 2‐Amino‐5‐(2,4‐dihydroxyphenyl)‐1,3,4‐thiadiazoles in Various Human Cancer Cells
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Quantitative Structure–Activity Relationship (QSAR) modeling of juvenile hormone activity: Comparison of validation procedures
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QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents
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Molecular fields in quantitative structure–permeation relationships: the VolSurf approach
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PubChem: Integrated Platform of Small Molecules and Biological Activities
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Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships
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Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)
inferredcites →https://doi.org/10.1021/ci100169popenalex-snapshot
Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
inferredcites →https://doi.org/10.1021/ci2005548openalex-snapshot
Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces
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Dependence of QSAR Models on the Selection of Trial Descriptor Sets: A Demonstration Using Nanotoxicity Endpoints of Decorated Nanotubes
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Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
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Development and Validation of AMANDA, a New Algorithm for Selecting Highly Relevant Regions in Molecular Interaction Fields
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Data Mining the NCI60 to Predict Generalized Cytotoxicity
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Suitability of GRIND-Based Principal Properties for the Description of Molecular Similarity and Ligand-Based Virtual Screening
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Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
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Quantitative Structure−Property Relationship for Predicting Chlorine Demand by Organic Molecules
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Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
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GRid-INdependent Descriptors (GRIND): A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors
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RAF inhibitors prime wild-type RAF to activate the MAPK pathway and enhance growth
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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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Systematic identification of genomic markers of drug sensitivity in cancer cells
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A community effort to assess and improve drug sensitivity prediction algorithms
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Subtype and pathway specific responses to anticancer compounds in breast cancer
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Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization
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Kernelized Bayesian Matrix Factorization
inferredcites →https://doi.org/10.1186/1471-2105-13-186openalex-snapshot
Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method
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Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
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Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology
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Current Mathematical Methods Used in QSAR/QSPR Studies
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Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods
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Additional file 1 of GPDRP: a multimodal framework for drug response prediction with graph transformer
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Network-based machine learning methods to model variability across individuals
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