A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization

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

Access to Document

acsomega.0c00732
© 2020 American Chemical Society
Final published version, 1.82 MBLicence: CC BY

@article{be2a58c85449402c87e03b303c84ceaa,

title = "A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization",

author = "Jacqui Hamilton",

note = "{\textcopyright} 2020 American Chemical Society",

year = "2020",

month = apr,

day = "14",

doi = "10.1021/acsomega.0c00732",

language = "English",

volume = "5",

pages = "9510--9516",

journal = "ACS Omega",

issn = "2470-1343",

publisher = "American Chemical Society",

number = "16",

}

TY - JOUR

T1 - A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization

AU - Hamilton, Jacqui

PY - 2020/4/14

Y1 - 2020/4/14

U2 - 10.1021/acsomega.0c00732

DO - 10.1021/acsomega.0c00732

M3 - Article

SN - 2470-1343

VL - 5

SP - 9510

EP - 9516

JO - ACS Omega

JF - ACS Omega

IS - 16

ER -