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Deep generative models for peptide design
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DOI：10.1039/d1dd00024a.png)
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Abstract
En 中文
Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field. We present a review of deep generative models and their applications in peptide design.
Keywords:
VARIATIONAL AUTOENCODER
ANTIMICROBIAL PEPTIDES
DRUG DISCOVERY
NEURAL-NETWORK
DATABASE
UNCERTAINTY
PROTEINS
RESOURCE
DNA
Journal
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5.6
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997
Citations:
1.7K
Organization
Cited Papers
Molecular generative model based on conditional variational autoencoder for de novo molecular design
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