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

Mario Lovrić, Tomislav Đuričić, Han T. N. Tran, Hussain Hussain, Emanuel Lacić, Morten A. Rasmussen, Roman Kern*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.

Original languageEnglish
Article number758
Issue number8
Publication statusPublished - 2 Aug 2021


  • Autoencoder
  • Embeddings
  • Machine learning
  • Manifold learning
  • Principal component analysis
  • Rdkit
  • Tox21

ASJC Scopus subject areas

  • Drug Discovery
  • Molecular Medicine
  • Pharmaceutical Science

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