Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7486
Title: | The importance of the regression model in the structure-based prediction of protein-ligand binding |
Authors: | Li, Hongjian Prof. LEUNG Kwong Sak Wong, Man-Hon Ballester, Pedro J. |
Issue Date: | 2015 |
Publisher: | Springer Verlag |
Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8623, 2015, pp. 219-230 |
Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Abstract: | Docking is a key computational method for structure-based design of starting points in the drug discovery process. Recently, the use of non-parametric machine learning to circumvent modelling assumptions has been shown to result in a large improvement in the accuracy of docking. As a result, these machine-learning scoring functions are able to widely outperform classical scoring functions. The latter are characterized by their reliance on a predetermined theory-inspired functional form for the relationship between the variables that characterise the complex and its predicted binding affinity. In this paper, we demonstrate that the superior performance of machine-learning scoring functions comes from the avoidance of the functional form that all classical scoring functions assume. These scoring functions can now be directly applied to the docking poses generated by AutoDock Vina, which is expected to increase its accuracy. On the other hand, as it is well known that the assumption of additivity does not hold in some cases, it is expected that the described protocol will also improve other classical scoring functions, as it has been the case with Vina. Lastly, results suggest that incorporating ligand- and protein-only properties into a model is a promising avenue for future research. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/7486 |
ISBN: | 978-331924461-7 |
ISSN: | 03029743 |
DOI: | 10.1007/978-3-319-24462-4_19 |
Appears in Collections: | Applied Data Science - Publication |
Find@HKSYU Show full item record
SCOPUSTM
Citations
4
checked on Dec 15, 2024
Page view(s)
43
Last Week
0
0
Last month
checked on Dec 20, 2024
Google ScholarTM
Impact Indices
Altmetric
PlumX
Metrics
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.