Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

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Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

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im Januar 2018 veröffentlichter wissenschaftlicher Artikel

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scientific article published on 24 September 2018

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wetenschappelijk artikel

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наукова стаття, опублікована у 2018

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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P1476

Empirical Scoring Functions fo ...... itical Aspects, and Challenges

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P2093

Felipe S S Pereira

http://www.w3.org/2001/XMLSchema#string

Laurent E Dardenne

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P2860

The Protein Data Bank

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Molecular docking and structure-based drug design strategies

A model binding site for testing scoring functions in molecular docking

Non-additivity of functional group contributions in protein-ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry

Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge

Energetic cost and structural consequences of burying a hydroxyl group within the core of a protein determined from Ala-->Ser and Val-->Thr substitutions in T4 lysozyme

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance

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1089

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scholarly article

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10.3389/FPHAR.2018.01089

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English

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P50

Isabella A Guedes

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2018-09-24T00:00:00Z

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30319422

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Farmakologi

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6165880

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