Title |
Interpreting linear support vector machine models with heat map molecule coloring
|
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Published in |
Journal of Cheminformatics, March 2011
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DOI | 10.1186/1758-2946-3-11 |
Pubmed ID | |
Authors |
Lars Rosenbaum, Georg Hinselmann, Andreas Jahn, Andreas Zell |
Abstract |
Model-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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 2 | 2% |
India | 1 | 1% |
Unknown | 76 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 19% |
Student > Ph. D. Student | 13 | 16% |
Student > Master | 8 | 10% |
Student > Bachelor | 8 | 10% |
Other | 7 | 9% |
Other | 13 | 16% |
Unknown | 17 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 17 | 21% |
Computer Science | 14 | 17% |
Agricultural and Biological Sciences | 10 | 12% |
Engineering | 7 | 9% |
Medicine and Dentistry | 2 | 2% |
Other | 11 | 14% |
Unknown | 20 | 25% |