Designing novel functional peptides by manipulating a temperature in the softmax function coupled with variational autoencoder

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Development of an efficient peptide design method is crucial for tackling medical problems, such as designing antimicrobial peptides for combating drug-resistant pathogens and anticancer peptides for various cancers. Here, we present Variational Autoencoder (VAE) coupled with a Softmax function having a temperature factor (T) for high-throughput design of novel functional peptides. VAE is a generative machine learning model, which has proved to be useful for generating peptide sequences. In this study, we additionally use a Softmax function with T to facilitate determining the most probable amino acids at each position of peptide sequences to be generated, which is difficult to achieve using a conventional VAE. In particular, by manipulating T in the Softmax function, we select biologically most feasible peptides with a desired function. This method is demonstrated for designing novel antimicrobial and anticancer peptides in this study. The method presented herein should be useful for designing various peptides with a desired function upon availability of relevant datasets.
Issue Date

2019 IEEE International Conference on Big Data, Big Data 2019, pp.6010 - 6012

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CBE-Conference Papers(학술회의논문)
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