Artificial intelligence in drug design:
This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Humana Press
[2022]
|
| Schriftenreihe: | Methods in molecular biology
2390 |
| Schlagworte: | |
| Zusammenfassung: | This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future? Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers |
| Beschreibung: | xi, 529 Seiten Illustrationen, Diagramme 254 mm |
| ISBN: | 9781071617861 |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV047447778 | ||
| 003 | DE-604 | ||
| 005 | 20220831 | ||
| 007 | t | ||
| 008 | 210831s2022 a||| |||| 00||| eng d | ||
| 020 | |a 9781071617861 |c hbk. |9 978-1-0716-1786-1 | ||
| 024 | 3 | |a 9781071617861 | |
| 035 | |a (OCoLC)1291612718 | ||
| 035 | |a (DE-599)BVBBV047447778 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T |a DE-19 | ||
| 084 | |a VC 6150 |0 (DE-625)147084:253 |2 rvk | ||
| 100 | 1 | |a Heifetz, Alexander |e Verfasser |0 (DE-588)1228295727 |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence in drug design |c edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK) |
| 264 | 1 | |a New York, NY |b Humana Press |c [2022] | |
| 300 | |a xi, 529 Seiten |b Illustrationen, Diagramme |c 254 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Methods in molecular biology |v 2390 | |
| 490 | 0 | |a Springer protocols | |
| 520 | |a This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future? Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers | ||
| 650 | 4 | |a Pharmacology | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Medicine | |
| 653 | |a Hardcover, Softcover / Medizin/Pharmazie | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-0716-1787-8 |
| 830 | 0 | |a Methods in molecular biology |v 2390 |w (DE-604)BV035362695 |9 2390 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032849816 | ||
Datensatz im Suchindex
| _version_ | 1804182743295197184 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Heifetz, Alexander |
| author_GND | (DE-588)1228295727 |
| author_facet | Heifetz, Alexander |
| author_role | aut |
| author_sort | Heifetz, Alexander |
| author_variant | a h ah |
| building | Verbundindex |
| bvnumber | BV047447778 |
| classification_rvk | VC 6150 |
| ctrlnum | (OCoLC)1291612718 (DE-599)BVBBV047447778 |
| discipline | Chemie / Pharmazie |
| discipline_str_mv | Chemie / Pharmazie |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02615nam a2200409 cb4500</leader><controlfield tag="001">BV047447778</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220831 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">210831s2022 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781071617861</subfield><subfield code="c">hbk.</subfield><subfield code="9">978-1-0716-1786-1</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9781071617861</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1291612718</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047447778</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">VC 6150</subfield><subfield code="0">(DE-625)147084:253</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Heifetz, Alexander</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1228295727</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence in drug design</subfield><subfield code="c">edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Humana Press</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xi, 529 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">254 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Methods in molecular biology</subfield><subfield code="v">2390</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Springer protocols</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future? Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pharmacology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Hardcover, Softcover / Medizin/Pharmazie</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-0716-1787-8</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Methods in molecular biology</subfield><subfield code="v">2390</subfield><subfield code="w">(DE-604)BV035362695</subfield><subfield code="9">2390</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032849816</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047447778 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:02:48Z |
| indexdate | 2024-07-10T09:12:24Z |
| institution | BVB |
| isbn | 9781071617861 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032849816 |
| oclc_num | 1291612718 |
| open_access_boolean | |
| owner | DE-29T DE-19 DE-BY-UBM |
| owner_facet | DE-29T DE-19 DE-BY-UBM |
| physical | xi, 529 Seiten Illustrationen, Diagramme 254 mm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Humana Press |
| record_format | marc |
| series | Methods in molecular biology |
| series2 | Methods in molecular biology Springer protocols |
| spelling | Heifetz, Alexander Verfasser (DE-588)1228295727 aut Artificial intelligence in drug design edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK) New York, NY Humana Press [2022] xi, 529 Seiten Illustrationen, Diagramme 254 mm txt rdacontent n rdamedia nc rdacarrier Methods in molecular biology 2390 Springer protocols This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future? Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers Pharmacology Artificial intelligence Machine learning Medicine Hardcover, Softcover / Medizin/Pharmazie (DE-588)4143413-4 Aufsatzsammlung gnd-content Erscheint auch als Online-Ausgabe 978-1-0716-1787-8 Methods in molecular biology 2390 (DE-604)BV035362695 2390 |
| spellingShingle | Heifetz, Alexander Artificial intelligence in drug design Methods in molecular biology Pharmacology Artificial intelligence Machine learning Medicine |
| subject_GND | (DE-588)4143413-4 |
| title | Artificial intelligence in drug design |
| title_auth | Artificial intelligence in drug design |
| title_exact_search | Artificial intelligence in drug design |
| title_exact_search_txtP | Artificial intelligence in drug design |
| title_full | Artificial intelligence in drug design edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK) |
| title_fullStr | Artificial intelligence in drug design edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK) |
| title_full_unstemmed | Artificial intelligence in drug design edited by Alexander Heifetz (Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK) |
| title_short | Artificial intelligence in drug design |
| title_sort | artificial intelligence in drug design |
| topic | Pharmacology Artificial intelligence Machine learning Medicine |
| topic_facet | Pharmacology Artificial intelligence Machine learning Medicine Aufsatzsammlung |
| volume_link | (DE-604)BV035362695 |
| work_keys_str_mv | AT heifetzalexander artificialintelligenceindrugdesign |