Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT

Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT

Abstract Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific struc...

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Bibliographic Details
Main Authors: Xinhao Li, Denis Fourches
Format: Article
Language:English
Published: BMC 2020-04-01
Series:Journal of Cheminformatics
Subjects:
Transfer learning
Neural networks
Self-supervised learning
QSPR/QSAR
Online Access:http://link.springer.com/article/10.1186/s13321-020-00430-x

Internet

http://link.springer.com/article/10.1186/s13321-020-00430-x

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