计算机科学
可扩展性
管道(软件)
云计算
化学空间
指纹(计算)
信息学
软件部署
人工智能
数据科学
软件工程
数据库
药物发现
生物信息学
程序设计语言
生物
操作系统
电气工程
工程类
作者
Christopher Kuenneth,Rampi Ramprasad
标识
DOI:10.1038/s41467-023-39868-6
摘要
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.
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