preprints.org > life sciences > biochemistry > doi: 10.20944/preprints202201.0365.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 24 January 2022 / Approved: 25 January 2022 / Online: 25 January 2022 (07:53:01 CET)
Version 2 : Received: 16 February 2022 / Approved: 16 February 2022 / Online: 16 February 2022 (02:57:38 CET)
Version 3 : Received: 23 May 2022 / Approved: 23 May 2022 / Online: 23 May 2022 (11:16:49 CEST)

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 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 dif-ferent deep learning approaches. This implies that continued accumulation of high-quality affinity data, especially for proteins without any affinity data, is important for improving deep learning models to better predict protein-ligand binding affinities.

binding affinity prediction; machine learning; data quality; data quantity; deep learning

LIFE SCIENCES, Biochemistry