Predicting Successes and Failures of Clinical Trials With Outer Product-Based Convolutional Neural Network
Sangwoo Seo1, Youngmin Kim2, Hyo-Jeong Han3
1Department of Data and Knowledge Service Engineering, Dankook University, Gyeonggido, Korea.
Frontiers in Pharmacology
|July 5, 2021
Summary
Predicting clinical trial success is crucial for drug discovery. A new outer product-based convolutional neural network (OPCNN) model accurately identifies potential drug failures, improving the drug development pipeline.
Area of Science:
- Computational chemistry and cheminformatics
- Biomedical data science
- Drug discovery and development
Background:
- Increasing rates of clinical trial failures due to adverse effects necessitate improved prediction models.
- Early identification of non-viable drug candidates is essential to optimize the drug discovery pipeline.
- Existing methods require enhancement for reliable prediction of clinical trial outcomes.
Purpose of the Study:
- To develop a robust model for predicting the outcomes of drug candidates in clinical trials.
- To integrate chemical and target-based drug features for enhanced predictive accuracy.
- To guide the drug discovery process by identifying potential 'loser' drug candidates early.
Main Methods:
- Development and application of an outer product-based convolutional neural network (OPCNN) model.
- Integration of drug chemical features and target-based features within the OPCNN framework.
- Validation using 10-fold cross-validation on the PrOCTOR dataset.
Main Results:
- The OPCNN model demonstrated high performance across multiple metrics: accuracy (0.9758), F1-score (0.9868), MCC (0.8451), precision (0.9889), recall (0.9893), AUC (0.9824), and AUPRC (0.9979).
- The model achieved superior performance in Matthews correlation coefficient (MCC), a key biomedical metric.
- Validation confirmed the OPCNN model's significant predictive power for clinical trial outcomes.
Conclusions:
- The proposed OPCNN model offers a reliable and accurate method for predicting clinical trial success.
- This approach can significantly aid in the early stages of drug discovery by filtering unpromising candidates.
- The OPCNN model represents a valuable tool for improving the efficiency and success rate of drug development.
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