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Cheminformatics, QSAR and machine learning applications for novel drug development /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Cheminformatics, QSAR and machine learning applications for novel drug development // edited by Kunal Roy.
Published:	London ;Academic Press, an imprint of Elsevier, : c2023.,
Description:	xvii, 745 p. :ill. (some col.) ;24 cm.
Subject:	Drugs - Structure-activity relationships. -
ISBN:	9780443186387

Cheminformatics, QSAR and machine learning applications for novel drug development /
Cheminformatics, QSAR and machine learning applications for novel drug development /edited by Kunal Roy. - London ;Academic Press, an imprint of Elsevier,c2023. - xvii, 745 p. :ill. (some col.) ;24 cm.

Includes bibliographical references and index.

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.

ISBN: 9780443186387US200.00Subjects--Topical Terms:

686448
Drugs
--Structure-activity relationships.

LC Class. No.: RM301.42 / .C54 2023

Cheminformatics, QSAR and machine learning applications for novel drug development /
LDR:02320cam a2200193 a 4500 001 2367332
008 241125s2023 enka b 001 0 eng d
020 $a 9780443186387 $q (pbk.) : $c US200.00
020 $a 0443186383 $q (pbk.)
020 $z 0443186391 $q (ebk.)
020 $z 9780443186394 $q (ebk.)
035 $a (OCoLC)1380392794
035 $a (OCoLC)scd1380392794
040 $a YDX $b eng $e aacr2 $c YDX $d OPELS $d UKMGB $d UKAHL $d OCLCF $d OCLCO $d YDX
050 # 4 $a RM301.42 $b .C54 2023
245 0 0 $a Cheminformatics, QSAR and machine learning applications for novel drug development / $c edited by Kunal Roy.
260 # $a London ; $a San Diego, CA : $b Academic Press, an imprint of Elsevier, $c c2023.
300 $a xvii, 745 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references and index.
520 # $a Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.
650 # 0 $a Drugs $x Structure-activity relationships. $3 686448
650 # 0 $a Drug development. $3 716904
650 # 0 $a QSAR (Biochemistry) $3 684832
650 # 0 $a Cheminformatics. $3 605686
650 # 0 $a Machine learning $x Therapeutic use. $3 3609389