Compression of molecular fingerprints with autoencoder networks

Author:

Ilnicka Agnieszka1ORCID,Schneider Gisbert12ORCID

Affiliation:

1. Department of Chemistry and Applied Biosciences ETH Zürich Vladimir-Prelog-Weg 4 8093 Zurich Switzerland

2. ETH Singapore SEC Ltd CREATE Way, #06-01 CREATE Tower Singapore Singapore

Abstract

AbstractSeveral binary molecular fingerprints were compressed using an autoencoder neural network. We analyzed the impact of compression on fingerprint performance in downstream classification and regression tasks. Classifiers trained on compressed fingerprints were negligibly affected. Regression models benefitted from compression, especially of long fingerprints (Morgan, RDK). However, their performance dropped rapidly for compression levels exceeding 90 %. Property co‐learning positively influenced the predictive power of the compressed fingerprints, with a mean score improvement up to 20 %, suggesting that autoencoder compression with property co‐learning biases the molecular representation toward the predicted target, facilitating downstream training.

Publisher

Wiley

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,Molecular Medicine,Structural Biology

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