Distributed Representation of Chemical Fragments

By Suman K. Chakravarti. 2018

30 citations recorded by the source corpus.

This article describes an unsupervised machine learning method for computing distributed vector representation of molecular fragments. These vectors encode fragment features in a continuous high-dimensional space and enable similarity computation between individual fragments, even for small fragments with only two heavy atoms. The method is based on a word embedding algorithm borrowed from natural language processing field, and approximately 6 million unlabeled PubChem chemicals were used for training. The resulting dense fragment vectors are in contrast to the traditional sparse “one-hot” fragment representation and capture rich relational structure in the fragment space. The vectors of small linear fragments were averaged to yield distributed vectors of bigger fragments and molecules, which were used for different tasks, e.g., clustering, ligand recall, and quantitative structure–activity relationship modeling. The distributed vectors were found to be better at clustering ring systems and recall of kinase ligands as compared to standard binary fingerprints. This work demonstrates unsupervised learning of fragment chemistry from large sets of unlabeled chemical structures and subsequent application to supervised training on relatively small data sets of labeled chemicals.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy, Representation & Self-Supervised Learning, Computational Chemistry & Drug Discovery

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Citations in the corpus

Strategies for Pre-training Graph Neural Networks (2019, background): “A number of recent works have also explored how node embeddings generalize across tasks (Jaeger et al., 2018; Zhou et al., 2018; Chakravarti, 2018; Narayanan et al., 2016).”
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “These include diﬀerent approaches for (i) understanding and controlling chemical systems and related behavior (Chakravarti, 2018; Fuchs et al., 2018; Janet et al., 2018; Elton et al., 2019; Mezei and Von Lilienfeld, 2019; Sanchez-Lengeling et al., 2019; Venkatasubramanian, 2019; Xu et al., 2019;…”