polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures. The polymer universe is gigantic. Searching this space effectively requires ultrafast high-fidelity property prediction methods. Here, the authors present a chemical language model that can probe this space at unprecedented speed and accuracy.

Automated summary

Polymers are integral to everyday life and researching suitable application-specific candidates is challenging. The authors propose a machine-driven polymer informatics pipeline, including polyBERT chemical fingerprinting and multitask learning, to search for candidates quickly and accurately. This pipeline outperforms existing methods in speed by two orders of magnitude while maintaining accuracy, making it suitable for scalable architectures like cloud infrastructures.