The aim of the talktorial is to perform one-hot encoding of SMILES structures on a subset of the ChEMBL data set to gain a deeper understanding on the one-hot encoding concept and why it is useful as a pre-processing step in various machine learning algorithms. Yonghui Chen, 2020, Volkamer lab, CharitéĪndrea Volkamer, 2020, Volkamer lab, Charité Sakshi Misra, CADD seminar 2020, Charité/FU Berlin Keras implementation of one-hot encodingĭeveloped in the CADD seminar 2020, Volkamer Lab, Charité/FU Berlin.Padding before one-hot encoding is performed.Padding after one-hot encoding is performed.Scikit-learn implementation of one-hot encoding.
Advantages and disadvantages of one-hot encoding.How to convert categorical data to numerical data?.What is the problem with categorical data?.
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