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|a 615.1900113
|2 23
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|a Virtual screening and drug docking /
|c Edited by Julio Caballero.
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| 260 |
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|a [S.l.] :
|b Academic Press,
|c 2022.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|2 rdacontent
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| 337 |
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|a computer
|2 rdamedia
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| 338 |
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|a online resource
|2 rdacarrier
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| 490 |
1 |
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|a Annual reports in medicinal chemistry ;
|v v. 59
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| 588 |
0 |
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|a Print version record.
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|a Intro -- Virtual Screening and Drug Docking -- Copyright -- Contents -- Contributors -- Preface -- Chapter One: Can docking scoring functions guarantee success in virtual screening? -- 1. Introduction -- 2. Scoring functions -- 3. Classifications of the scoring functions -- 3.1. Classical scoring functions -- 3.1.1. Force field/physics-based scoring functions -- 3.1.2. Empirical scoring functions -- 3.1.3. Knowledge-based scoring functions -- 3.2. Machine learning scoring functions -- 4. Strengths and weaknesses of classical scoring functions -- 5. Comparative studies of scoring functions -- 6. Strategies to improve the performance of scoring functions in virtual screening -- 7. How to choose a scoring function for virtual screening? -- 8. Future perspectives in scoring functions -- 9. Conclusions -- References -- Chapter Two: No dance, no partner! A tale of receptor flexibility in docking and virtual screening -- 1. Introduction -- 2. Theoretical framework -- 3. In silico methods to account for flexibility in molecular recognition -- 3.1. Molecular docking -- 3.2. MD-based methods for studying ligand-binding events -- 3.3. Machine learning methods to address protein flexibility -- 4. Conclusions and perspectives -- References -- Chapter Three: Using filters in virtual screening: A comprehensive guide to minimize errors and maximize efficiency -- 1. Introduction -- 2. Chemical space and ligand libraries -- 3. Types of databases for virtual screening -- 4. Ligand library design and selection -- 5. Constructing 3D structures-Ligand preparation -- 6. Druglikeness vs leadlikeness -- 6.1. Drug-like rules (Lipinski, Ghose, Egan, Muegge, Veber) -- 6.2. Quantitative estimate of druglikeness (QED) -- 6.3. Lead-like rules (Oprea) -- 7. Promiscuous inhibitors and frequent hitters -- 7.1. PAINS -- 7.2. Aggregators -- 7.3. Reactive and toxic functionalities.
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| 505 |
8 |
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|a 8. Knowledge-based filters -- 9. Pre and post VS tools -- 10. Machine learning and future directions -- References -- Chapter Four: Rational computational approaches to predict novel drug candidates against leishmaniasis -- 1. Introduction -- 2. Target-based approaches -- 2.1. Screening of compounds against Leishmania protein targets using a hybrid MD/docking approach -- 2.2. Implementation of a machine learning approach to classify druggable kinases in different Leishmania species -- 2.3. Repositioning of known drugs based on molecular homologue targets in different Leishmania species -- 3. Ligand-based approaches -- 3.1. Prediction of different types of toxicity for drug-like molecular candidates -- 3.2. Prediction of new drug purposes by ligand-based off-target activity predictions -- 4. Pharmacokinetics simulation -- 4.1. Population pharmacokinetics simulations of known drugs with potential anti-Leishmania activity -- 5. Conclusion -- Acknowledgments -- References -- Chapter Five: Virtual screening against Mycobacterium tuberculosis DNA gyrase: Applications and success stories -- 1. Introduction -- 2. The Mtb gyrase heterotetramer: Unique catalysis -- 2.1. Sites of Mtb gyrase inhibition -- 2.2. Resisting resistance: When gyrase goes rogue -- 3. Virtual screening in anti-TB drug discovery -- 4. Structure-guided virtual screening -- 4.1. Receptor-based pharmacophore modeling and lead optimization against GyrB -- 4.1.1. From an aminopyrazinamide-bound receptor -- 4.1.2. Pharmacophore modeling and validation -- 4.1.3. Pharmacophore model- and docking-based virtual screening -- 4.1.4. In vitro validation -- 4.1.5. Lead expansion -- 4.2. Receptor-based pharmacophore modeling and lead optimization against GyrB -- 4.2.1. From a pyrrolamide inhibitor-bound receptor -- 4.2.2. Pharmacophore- and docking-based virtual screening -- 4.2.3. In vitro validation.
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| 505 |
8 |
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|a 4.2.4. Lead optimization -- 4.3. Consensus docking and lead optimization: GyrB inhibitor discovery -- 4.3.1. Docking-based virtual screening and in vitro validation -- 4.3.2. Lead optimization -- 5. Ligand-based virtual screening -- 6. Applications of molecular docking: Designing NBTIs -- 6.1. Benzimidazole scaffold morphing -- 6.2. LHS substitutions of NBTIs -- 7. Docking-based identification of non-fluoroquinolone inhibitors of GyrA -- 8. Conclusion -- References.
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| 650 |
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|a Drug development
|x Computer simulation.
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| 650 |
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0 |
|a Drugs
|x Structure-activity relationships
|x Computer simulation.
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| 650 |
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6 |
|a M�edicaments
|0 (CaQQLa)201-0306620
|x D�eveloppement
|0 (CaQQLa)201-0306620
|x Simulation par ordinateur.
|0 (CaQQLa)201-0379159
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| 650 |
|
6 |
|a M�edicaments
|0 (CaQQLa)201-0028672
|x Relations structure-activit�e
|0 (CaQQLa)201-0028672
|x Simulation par ordinateur.
|0 (CaQQLa)201-0379159
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| 650 |
|
7 |
|a Drugs
|x Structure-activity relationships
|x Computer simulation
|2 fast
|0 (OCoLC)fst00898930
|
| 700 |
1 |
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|a Caballero, Julio.
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| 776 |
0 |
8 |
|i ebook version :
|z 9780323986052
|
| 776 |
0 |
8 |
|c Original
|z 0323985955
|z 9780323985956
|w (OCoLC)1309866895
|
| 776 |
0 |
8 |
|i Print version:
|t VIRTUAL SCREENING AND DRUG DOCKING.
|d [S.l.] : ELSEVIER ACADEMIC PRESS, 2022
|z 0323985955
|w (OCoLC)1309866895
|
| 830 |
|
0 |
|a Annual reports in medicinal chemistry ;
|v v. 59.
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/bookseries/00657743/59
|z Texto completo
|
