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Prediction of Activity Cliffs Using Support Vector Machines

https://doi.org/10.1021/ci300306a
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Graph kernels for chemical informatics
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Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier
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On Outliers and Activity CliffsWhy QSAR Often Disappoints
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From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis
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Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization
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MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs
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Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
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Characterization of Activity Landscapes Using 2D and 3D Similarity Methods: Consensus Activity Cliffs
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Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
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Design and Evaluation of Bonded Atom Pair Descriptors
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Activity Landscape Representations for Structure−Activity Relationship Analysis
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Exploring Activity Cliffs in Medicinal Chemistry
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SVMs Modeling for Highly Imbalanced Classification
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Comprehensive Analysis of Single‐ and Multi‐Target Activity Cliffs Formed by Currently Available Bioactive Compounds
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A training algorithm for optimal margin classifiers
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Proceedings of the 25th international conference on Machine learning - ICML '08
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Advances in kernel methods: support vector learning

What points at this work

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

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ACGCN: Graph Convolutional Networks for Activity Cliff Prediction between Matched Molecular Pairs
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Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs
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Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors
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Topic extraction to provide an overview of research activities: The case of the high-temperature superconductor and simulation and modelling
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Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity
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Additional file 1 of Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity
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Additional file 1 of Explaining compound activity predictions with a substructure-aware loss for graph neural networks
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