Literature DB >> 26481649

The statistics of virtual screening and lead optimization.

Mark McGann, Anthony Nicholls, Istvan Enyedy.   

Abstract

Analytic formulae are used to estimate the error for two virtual screening metrics, enrichment factor and area under the ROC curve. These analytic error estimates are then compared to bootstrapping error estimates, and shown to have excellent agreement with respect to area under the ROC curve and good agreement with respect to enrichment factor. The major advantage of the analytic formulae is that they are trivial to calculate and depend only on the number of actives and inactives and the measured value of the metric, information commonly reported in papers. In contrast to this, the bootstrapping method requires the individual compound scores. Methods for converting the error, which is calculated as a variance, into more familiar error bars are also discussed.

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Year:  2015        PMID: 26481649     DOI: 10.1007/s10822-015-9861-4

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  9 in total

1.  Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4.

Authors:  Nicolas Triballeau; Francine Acher; Isabelle Brabet; Jean-Philippe Pin; Hugues-Olivier Bertrand
Journal:  J Med Chem       Date:  2005-04-07       Impact factor: 7.446

Review 2.  The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data.

Authors:  A Ralph Henderson
Journal:  Clin Chim Acta       Date:  2005-09       Impact factor: 3.786

3.  FRED pose prediction and virtual screening accuracy.

Authors:  Mark McGann
Journal:  J Chem Inf Model       Date:  2011-02-16       Impact factor: 4.956

4.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1983-09       Impact factor: 11.105

5.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

6.  Confidence limits, error bars and method comparison in molecular modeling. Part 1: the calculation of confidence intervals.

Authors:  A Nicholls
Journal:  J Comput Aided Mol Des       Date:  2014-06-05       Impact factor: 3.686

Review 7.  What do we know and when do we know it?

Authors:  Anthony Nicholls
Journal:  J Comput Aided Mol Des       Date:  2008-02-06       Impact factor: 3.686

8.  Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search.

Authors:  Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2007-03-27       Impact factor: 4.179

Review 9.  Recommendations for evaluation of computational methods.

Authors:  Ajay N Jain; Anthony Nicholls
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

  9 in total
  3 in total

1.  Statistics in molecular modeling: a summary.

Authors:  Anthony Nicholls
Journal:  J Comput Aided Mol Des       Date:  2016-03-21       Impact factor: 3.686

2.  Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Authors:  Spencer S Ericksen; Haozhen Wu; Huikun Zhang; Lauren A Michael; Michael A Newton; F Michael Hoffmann; Scott A Wildman
Journal:  J Chem Inf Model       Date:  2017-07-12       Impact factor: 4.956

3.  SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

Authors:  Niraj Verma; Xingming Qu; Francesco Trozzi; Mohamed Elsaied; Nischal Karki; Yunwen Tao; Brian Zoltowski; Eric C Larson; Elfi Kraka
Journal:  Int J Mol Sci       Date:  2021-01-30       Impact factor: 5.923

  3 in total

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