Heck reaction prediction using a transformer model based on a transfer learning strategy

Ling Wang; Chengyun Zhang; Renren Bai; Jianjun Li; Hongliang Duan

doi:10.1039/d0cc02657c

Heck reaction prediction using a transformer model based on a transfer learning strategy

Ling Wang
, Chengyun Zhang
, Renren Bai
, Jianjun Li
, Hongliang Duan

Zhejiang University of Technology
Hangzhou Normal University
CAS - Shanghai Institute of Materia Medica

Research output: Contribution to journal › Article › peer-review

46 Citations (Scopus)

Abstract

A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented. We demonstrate this by applying transfer learning combined with the transformer model to small-dataset Heck reaction prediction. Introducing transfer learning significantly improved the accuracy of the transformer-transfer learning model (94.9%) over that of the transformer-baseline model (66.3%).

Original language	English
Pages (from-to)	9368-9371
Number of pages	4
Journal	Chemical Communications
Volume	56
Issue number	65
DOIs	https://doi.org/10.1039/d0cc02657c
Publication status	Published - 21 Aug 2020
Externally published	Yes

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10.1039/d0cc02657c

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title = "Heck reaction prediction using a transformer model based on a transfer learning strategy",

abstract = "A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented. We demonstrate this by applying transfer learning combined with the transformer model to small-dataset Heck reaction prediction. Introducing transfer learning significantly improved the accuracy of the transformer-transfer learning model (94.9\%) over that of the transformer-baseline model (66.3\%).",

author = "Ling Wang and Chengyun Zhang and Renren Bai and Jianjun Li and Hongliang Duan",

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year = "2020",

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AU - Wang, Ling

AU - Zhang, Chengyun

AU - Bai, Renren

AU - Li, Jianjun

AU - Duan, Hongliang

N1 - Publisher Copyright: © The Royal Society of Chemistry.

PY - 2020/8/21

Y1 - 2020/8/21

N2 - A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented. We demonstrate this by applying transfer learning combined with the transformer model to small-dataset Heck reaction prediction. Introducing transfer learning significantly improved the accuracy of the transformer-transfer learning model (94.9%) over that of the transformer-baseline model (66.3%).

AB - A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented. We demonstrate this by applying transfer learning combined with the transformer model to small-dataset Heck reaction prediction. Introducing transfer learning significantly improved the accuracy of the transformer-transfer learning model (94.9%) over that of the transformer-baseline model (66.3%).

UR - https://www.scopus.com/pages/publications/85089614578

U2 - 10.1039/d0cc02657c

DO - 10.1039/d0cc02657c

M3 - Article

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VL - 56

SP - 9368

EP - 9371

JO - Chemical Communications

JF - Chemical Communications

IS - 65

ER -

Heck reaction prediction using a transformer model based on a transfer learning strategy

Abstract

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