BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening

28 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Recent attempts at utilizing deep learning for structure-based virtual screening have focused on training models to predict binding affinity from protein-ligand complexes with known crystal structures. The PDBbind dataset is the current standard for training such models, but its small size (less than 20K binding affinity measurements) leads to models failing to generalize to new targets, and model performance is typically on par with those trained with only ligand information. The CrossDocked dataset expands binding pose data for protein-ligand complexes but does not introduce new affinity data. ChEMBL, on the other hand, contains a wealth of binding affinity information but contains no information about the binding poses. We introduce BigBind, a dataset that maps ChEMBL activity data to protein targets from CrossDocked. This dataset comprises 851K ligand binding affinities and 3D pocket structures. After augmenting this dataset with an equal number of putative inactives for each target, we train BANANA (BAsic NeurAl Network for binding Affinity) to classify actives from inactives. The resulting model achieved an AUC of 0.72 on BigBind’s test set, while a ligand-only model achieved an AUC of 0.64. Our model achieves competitive performance on the LIT-PCBA benchmark (median EF1% 2.06) while running 16,000 times faster than molecular docking with GNINA. Notably, we achieve a state-of-the-art EF1% of 4.95 when we use BANANA to filter out 90% of the compounds prior to docking with GNINA. We hope that BANANA and future models trained on this dataset will prove useful for prospective virtual screening tasks.

Supplementary materials

Title
Description
Actions
Title
Supporting Information: BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening
Description
Supporting information for the BigBind paper.
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.