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Machine learning for drug discovery/

American Chemical Society.

Machine learning for drug discovery/

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning for drug discovery/ / Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez.
Author:	Melo, Marcelo C.R.,
other author:	Fuente Nunez, Cesar de la,
Published:	Washington, DC, USA: American Chemical Society, : 2022,
基督教聖經之智慧書導讀 :	"Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included."--
Subject:	Drug Evaluation - methods. -
Online resource:	https://er.kmu.edu.tw/user/login/?next=/er/geter/EB000211602/
ISBN:	9780841299238(ebook):

Machine learning for drug discovery/
Melo, Marcelo C.R.,

Machine learning for drug discovery/ Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez. - Washington, DC, USA: American Chemical Society, 2022 - ACS in focus,2691-8307. - ACS in focus,.

Includes bibliographical references and index.

Pursuing New Models and Molecules --

"Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included."--

ISBN: 9780841299238(ebook): NT2900Subjects--Topical Terms:

521167
Drug Evaluation
--methods.

Machine learning for drug discovery/
LDR:01767nam a2200193 i 4500 001 389459
003 DACS
005 20220311120309.0
008 251103s2022 dcua ob 101 0 eng d
020 $a 9780841299238(ebook): $c NT2900
040 $a KMU
087 0 4 $a 418 $b M528
100 1 $a Melo, Marcelo C.R., $e author. $u University of Pennsylvania. $3 521164
245 0 0 $a Machine learning for drug discovery/ $c Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez.
260 # 1 $a Washington, DC, USA: $b American Chemical Society, $c 2022
490 1 $a ACS in focus, $x 2691-8307
504 $a Includes bibliographical references and index.
505 0 0 $t Pursuing New Models and Molecules -- $t Key Algorithms for Drug Discovery -- $t Data Representation in Computational Chemistry -- $t Drug-likeness Prediction -- $t Antimicrobial Activity Prediction -- $t Antimicrobial Resistance Prediction -- $t Generative Deep Learning for Drug Discovery -- $t Future Directions.
520 # $a "Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included."-- $c Provided by publisher.
590 $a American Chemical Society, Machine Learning for Drug Discovery eBooks - 2022 Front Files.
650 1 2 $a Drug Evaluation $x methods. $3 521167
650 # 0 $2 96060 $a Medical informatics. $3 237626
650 # 0 $2 96060 $a Machine learning. $3 248498
650 # 0 $a Drug development $x Data processing. $3 483121
650 # 0 $a Artificial intelligence $x Medical applications. $3 413438
700 1 # $a Fuente Nunez, Cesar de la, $e author. $u University of Pennsylvania. $3 521166
700 1 # $a Maasch, Jacqueline R. M. A., $e author. $u Cornell University. $3 521165
710 2 # $2 97100 $a American Chemical Society. $3 236335
830 0 $a ACS in focus, $x 2691-8307 $3 521128
856 4 # $u https://er.kmu.edu.tw/user/login/?next=/er/geter/EB000211602/