In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 9 ( 2023-9-5), p. e1011301-
Abstract:
Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model’s root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1011301
DOI:
10.1371/journal.pcbi.1011301.g001
DOI:
10.1371/journal.pcbi.1011301.g002
DOI:
10.1371/journal.pcbi.1011301.g003
DOI:
10.1371/journal.pcbi.1011301.g004
DOI:
10.1371/journal.pcbi.1011301.s001
DOI:
10.1371/journal.pcbi.1011301.s002
DOI:
10.1371/journal.pcbi.1011301.s003
DOI:
10.1371/journal.pcbi.1011301.s004
DOI:
10.1371/journal.pcbi.1011301.s005
DOI:
10.1371/journal.pcbi.1011301.s006
DOI:
10.1371/journal.pcbi.1011301.s007
DOI:
10.1371/journal.pcbi.1011301.s008
DOI:
10.1371/journal.pcbi.1011301.s009
DOI:
10.1371/journal.pcbi.1011301.s010
DOI:
10.1371/journal.pcbi.1011301.s011
DOI:
10.1371/journal.pcbi.1011301.s012
DOI:
10.1371/journal.pcbi.1011301.s013
DOI:
10.1371/journal.pcbi.1011301.s014
DOI:
10.1371/journal.pcbi.1011301.r001
DOI:
10.1371/journal.pcbi.1011301.r002
DOI:
10.1371/journal.pcbi.1011301.r003
DOI:
10.1371/journal.pcbi.1011301.r004
DOI:
10.1371/journal.pcbi.1011301.r005
DOI:
10.1371/journal.pcbi.1011301.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
Permalink