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Improved Machine Learning Models for Predicting Selective Compounds

https://doi.org/10.1021/ci200346b

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cmdc.200900300openalex-snapshot

From Structure–Activity to Structure–Selectivity Relationships: Quantitative Assessment, Selectivity Cliffs, and Key Compounds

inferredcites →https://doi.org/10.1007/978-1-4419-6045-0_19openalex-snapshot

Trends in Chemical Graph Data Mining

inferredcites →https://doi.org/10.1007/978-1-60761-839-3_20openalex-snapshot

Molecular Test Systems for Computational Selectivity Studies and Systematic Analysis of Compound Selectivity Profiles

inferredcites →https://doi.org/10.1007/978-1-60761-839-3_21openalex-snapshot

Application of Support Vector Machine-Based Ranking Strategies to Search for Target-Selective Compounds

inferredcites →https://doi.org/10.1021/ci800441copenalex-snapshot

Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors

inferredcites →https://doi.org/10.1021/ci900182qopenalex-snapshot

Multi-Assay-Based Structure−Activity Relationship Models: Improving Structure−Activity Relationship Models by Incorporating Activity Information from Related Targets

inferredcites →https://doi.org/10.1021/ci9800211openalex-snapshot

Chemical Similarity Searching

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The Correlation of Biological Activity of Plant Growth Regulators and Chloromycetin Derivatives with Hammett Constants and Partition Coefficients

inferredcites →https://doi.org/10.1038/194178b0openalex-snapshot

Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients

inferredcites →https://doi.org/10.1038/nbt1358openalex-snapshot

A quantitative analysis of kinase inhibitor selectivity

inferredcites →https://doi.org/10.1038/nrc2294openalex-snapshot

The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot

Statistical Learning Theory

inferredcites →https://doi.org/10.1111/j.1747-0285.2007.00554.xopenalex-snapshot

Methods for Computer‐aided Chemical Biology. Part 1: Design of a Benchmark System for the Evaluation of Compound Selectivity

inferredcites →https://doi.org/10.1111/j.1747-0285.2007.00555.xopenalex-snapshot

Methods for Computer‐aided Chemical Biology. Part 2: Evaluation of Compound Selectivity Using 2D Molecular Fingerprints

inferredcites →https://doi.org/10.1111/j.1747-0285.2008.00670.xopenalex-snapshot

Methods for Computer‐Aided Chemical Biology. Part 3: Analysis of Structure–Selectivity Relationships through Single‐ or Dual‐Step Selectivity Searching and Bayesian Classification

inferredcites →https://doi.org/10.1145/1102351.1102479openalex-snapshot

Learning Gaussian processes from multiple tasks

inferredcites →https://doi.org/10.1145/1273496.1273583openalex-snapshot

Multi-task learning for sequential data via iHMMs and the nested Dirichlet process

inferredcites →https://doi.org/10.1186/1471-2105-9-363openalex-snapshot

Virtual screening of GPCRs: An in silico chemogenomics approach

inferredcites →https://doi.org/10.5555/1046920.1088693openalex-snapshot

Learning Multiple Tasks with Kernel Methods

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Application of machine learning for advanced material prediction and design

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Multi-task learning models for predicting active compounds

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Materials discovery and design using machine learning

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The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform

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Differential Compound Prioritization via Bidirectional Selectivity Push with Power

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Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

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Exhaustive Proteome Mining for Functional MHC-I Ligands

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Selectivity Data: Assessment, Predictions, Concordance, and Implications

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Correlation analysis of modern analytical data – a chemometric dissection of spectral and chromatographic variables

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A Novel Approach to Evaluate the Quality and Identify the Active Compounds of the Essential Oil fromCurcuma longaL.

inferred← citeshttps://doi.org/10.1101/2022.07.10.499476openalex-snapshot

Machine learning approaches to quantitively predict selectivity of compounds against hDAC1 and hDAC6 isoforms

inferred← citeshttps://doi.org/10.1111/cbdd.12111openalex-snapshot

A Novel Approach to Active Compounds Identification Based on Support Vector Regression Model and Mean Impact Value

inferred← citeshttps://doi.org/10.1142/s2737416524500121openalex-snapshot

Machine Learning Approaches to Predict the Selectivity of Compounds against HDAC1 and HDAC6

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Harnessing Deep Belief Networks for Selective HDAC6 Inhibitors Identification

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An Algorithm to Identify Target-Selective Ligands – A Case Study of 5-HT7/5-HT1A Receptor Selectivity

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Learning Set Functions with Implicit Differentiation

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Using Deep Learning for Compound Selectivity Prediction

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PENERAPAN PENDEKATAN MACHINE LEARNING PADA PENGEMBANGAN BASIS DATA HERBAL SEBAGAI SUMBER INFORMASI KANDIDAT OBAT KANKER

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