Improving VAE based molecular representations for compound property prediction

Improving VAE based molecular representations for compound property prediction

Abstract Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to...

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Bibliographic Details
Main Authors: Ani Tevosyan, Lusine Khondkaryan, Hrant Khachatrian, Gohar Tadevosyan, Lilit Apresyan, Nelly Babayan, Helga Stopper, Zaven Navoyan
Format: Article
Language:English
Published: BMC 2022-10-01
Series:Journal of Cheminformatics
Subjects:
Variational autoencoders
Vector representation
Transfer learning
Property prediction
Online Access:https://doi.org/10.1186/s13321-022-00648-x

Internet

https://doi.org/10.1186/s13321-022-00648-x

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