Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors.

ABSTRACT: The extreme surge of interest over the past decade surrounding the use of neural networks has inspired many groups to deploy them for predicting binding affinities of drug-like molecules to their receptors. A model that can accurately make such predictions has the potential to screen large chemical libraries and help streamline the drug discovery process. However, despite reports of models that accurately predict quantitative inhibition using protein kinase sequences and inhibitors' SMILES strings, it is still unclear whether these models can generalize to previously unseen data. Here, we build a Convolutional Neural Network (CNN) analogous to those previously reported and evaluate the model over four datasets commonly used for inhibitor/kinase predictions. We find that the model performs

SUBMITTER: Ong WJG 

PROVIDER: S-EPMC10508770 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature