Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction.

Autor: Fan FJ; School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, Queensland 4670, Australia., Shi Y; Institute for Glycomics, Griffith University, Southport, Queensland 4222, Australia. Electronic address: y.shi@griffith.edu.au.
Jazyk: angličtina
Zdroj: Bioorganic & medicinal chemistry [Bioorg Med Chem] 2022 Oct 15; Vol. 72, pp. 117003. Date of Electronic Publication: 2022 Sep 09.
DOI: 10.1016/j.bmc.2022.117003
Abstrakt: Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of these systems have advanced to some degrees depending on the dataset used for model training and testing, the effects of the quality and quantity of the underlying data have not been thoroughly examined. In this study, we employed erroneous datasets and data subsets of different sizes, created from one of the largest databases of experimental binding affinities, to train and evaluate a deep learning system based on convolutional neural networks. Our results show that data quality and quantity do have significant impacts on the prediction performance of trained models. Depending on the variations in data quality and quantity, the performance discrepancies could be comparable to or even larger than those observed among different deep learning approaches. In particular, the presence of proteins in the training data leads to a dramatic increase in prediction accuracy. This implies that continued accumulation of high-quality affinity data, especially for new protein targets, is indispensable for improving deep learning models to better predict protein-ligand binding affinities.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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