Interpreting linear support vector machine models with heat map molecule coloring

By Lars Rosenbaum, Georg Hinselmann, Andreas Jahn, Andreas Zell. 2011

54 citations recorded by the source corpus.

BackgroundModel-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.ResultsWe evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.ConclusionsIn combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.

Subfields: Biophysics, Computational Theory and Mathematics, Materials Chemistry, Safety, Robustness & Interpretability, Computational Chemistry & Drug Discovery

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Natalia Díaz Rodríguez

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “In [68] a visual way combines the output of the SVM with heatmaps to guide the modiﬁcation of compounds in late stages of drug discovery.”
Interpreting random forest classification models using a feature contribution method (2013, background): “atures contribute the most to classify a speciﬁc instance? Answers to these questions are considered particularly valuable in such domains as chemoinformatics, bioinformatics or predictive toxicology [15]. Linear models, which assign instance-independent coefﬁcients to all features, are the most eas”
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data (2017, methodology): “Several approaches in the literature have been proposed for interpreting local and global QSAR models based on support vector machine and random forest techniques [46, 47].”