Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine l...

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Bibliographic Details
Main Authors: Mario Lovrić, Tomislav Đuričić, Han T. N. Tran, Hussain Hussain, Emanuel Lacić, Morten A. Rasmussen, Roman Kern
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Pharmaceuticals
Subjects:
manifold learning
machine learning
rdkit
embeddings
Tox21
principal component analysis
Online Access:https://www.mdpi.com/1424-8247/14/8/758

Internet

https://www.mdpi.com/1424-8247/14/8/758

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