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Predicting Successes and Failures of Clinical Trials With Outer Product-Based Convolutional Neural Network

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Abstract
Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product-based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision-recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.
Author(s)
서상우김영민한효정손우찬홍첸유손인석심주용황창하
Issued Date
2021
Type
Article
Keyword
clinical trialconvolutional neural networkimbalancemultimodal learningouter productPharmacology
DOI
10.3389/fphar.2021.670670
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7456
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_15e29c2f043c42ddbb91255c08c6ce57&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Predicting%20Successes%20and%20Failures%20of%20Clinical%20Trials%20With%20Outer%20Product-Based%20Convolutional%20Neural%20Network&offset=0&pcAvailability=true
Publisher
FRONTIERS IN PHARMACOLOGY
Location
스위스
Language
한국어
ISSN
1663-9812
Citation Volume
12
Citation Number
00
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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