Literature DB >> 21591735

Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": significance for the validation of scoring functions.

Pedro J Ballester1, John B O Mitchell.   

Abstract

Mesh：

Year:  2011        PMID： 21591735     DOI： 10.1021/ci200057e

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN： 1549-9596            Impact factor:   4.956


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  11 in total

1.  Just Add Data: automated predictive modeling for knowledge discovery and feature selection.

Authors:  Ioannis Tsamardinos; Paulos Charonyktakis; Georgios Papoutsoglou; Giorgos Borboudakis; Kleanthi Lakiotaki; Jean Claude Zenklusen; Hartmut Juhl; Ekaterini Chatzaki; Vincenzo Lagani
Journal:  NPJ Precis Oncol       Date:  2022-06-16

2.  Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Authors:  Paul G Francoeur; Tomohide Masuda; Jocelyn Sunseri; Andrew Jia; Richard B Iovanisci; Ian Snyder; David R Koes
Journal:  J Chem Inf Model       Date:  2020-09-10       Impact factor: 4.956

3.  Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification.

Authors:  Pedro J Ballester; Martina Mangold; Nigel I Howard; Richard L Marchese Robinson; Chris Abell; Jochen Blumberger; John B O Mitchell
Journal:  J R Soc Interface       Date:  2012-08-29       Impact factor: 4.118

4.  Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

Authors:  Hongjian Li; Kwong-Sak Leung; Man-Hon Wong; Pedro J Ballester
Journal:  BMC Bioinformatics       Date:  2014-08-27       Impact factor: 3.169

5.  Performance of machine-learning scoring functions in structure-based virtual screening.

Authors:  Maciej Wójcikowski; Pedro J Ballester; Pawel Siedlecki
Journal:  Sci Rep       Date:  2017-04-25       Impact factor: 4.379

6.  The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

Authors:  Hongjian Li; Jiangjun Peng; Yee Leung; Kwong-Sak Leung; Man-Hon Wong; Gang Lu; Pedro J Ballester
Journal:  Biomolecules       Date:  2018-03-14

7.  Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

Authors:  Hossam M Ashtawy; Nihar R Mahapatra
Journal:  BMC Bioinformatics       Date:  2015-04-17       Impact factor: 3.169

8.  Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?

Authors:  Pedro J Ballester; Adrian Schreyer; Tom L Blundell
Journal:  J Chem Inf Model       Date:  2014-02-20       Impact factor: 4.956

9.  istar: a web platform for large-scale protein-ligand docking.

Authors:  Hongjian Li; Kwong-Sak Leung; Pedro J Ballester; Man-Hon Wong
Journal:  PLoS One       Date:  2014-01-24       Impact factor: 3.240

Review 10.  Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

Authors:  Qurrat Ul Ain; Antoniya Aleksandrova; Florian D Roessler; Pedro J Ballester
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2015-08-28
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