Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials

作     者：Samantha Stuart Jeffrey Watchorn Frank X.Gu 

作者机构：Institute of Biomedical EngineeringUniversity of TorontoTorontoOntarioCanada Department of Chemical Engineering and Applied ChemistryUniversity of TorontoTorontoOntarioCanada 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：1287-1296页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by NSERC Discovery Grant#06441 and the NSERC Senior Industrial Research Chair program.S.S.is supported by the NSERC Alexander Graham Bell Canada Graduate Scholarship and the Canadian Federation of University Women 1989 Ecole Polytechnique Commemorative Award.J.W.is supported by Queen Elizabeth II/Dupont Canada Scholarship in Science and Technology and the Mclean Foundation Graduate Scholarships in Science And Technology 

主　　题：properties prediction biomaterials 

摘      要：It has proved challenging to represent the behavior of polymeric macromolecules as machine learning features for biomaterial interaction *** are several approaches to this representation,yet no consensus for a universal representational framework,in part due to the sensitivity of biomacromolecular interactions to polymer *** help navigate the process of feature engineering,we provide an overview of popular classes of data representations for polymeric biomaterial machine learning while discussing their merits and ***,increasing the accessibility of polymeric biomaterial feature engineering knowledge will contribute to the goal of accelerating clinical translation from biomaterials discovery.