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Machine learning optimization of cross docking accuracy

Paper ID Volume ID Publish Year Pages File Format Full-Text
14921 1361 2016 12 PDF Available
Title
Machine learning optimization of cross docking accuracy
Abstract

•Machine learning method for optimizing docking functions.•Alternative score weights for cross-docking with Autodock Vina and Smina.•Cross-docking benchmark for realistic estimation of ligand pose prediction accuracy.

Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening capabilities of the docking program, respectively. Benchmarks show that different docking programs can excel in individual benchmarks which suggests that the scoring function employed by the programs can be optimized for a particular task. Here the scoring function of Smina is re-optimized towards enhancing the docking power using a supervised machine learning approach and a manually curated database of ligands and cross docking receptor pairs. The optimization method does not need associated binding data for the receptor-ligand examples used in the data set and works with small train sets. The re-optimization of the weights for the scoring function results in a similar docking performance with regard to docking power towards a cross docking test set. A ligand decoy based benchmark indicates a better discrimination between poses with high and low RMSD. The reported parameters for Smina are compatible with Autodock Vina and represent ready-to-use alternative parameters for researchers who aim at pose prediction rather than affinity prediction.

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Keywords
Molecular docking; Docking power; Scoring function; Machine learning optimization; Smina; Autodock Vina; Drug discovery; Cross docking
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Machine learning optimization of cross docking accuracy
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 62, June 2016, Pages 133–144
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us