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A neural network-based method for polypharmacy side effects prediction.

Raziyeh Masumshah1, Rosa Aghdam2, Changiz Eslahchi3,4

  • 1Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.

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|July 25, 2021
PubMed
Summary
This summary is machine-generated.

A new neural network method (NNPS) accurately predicts polypharmacy side effects by analyzing drug interactions. This computational approach significantly improves upon existing methods in speed and performance, enhancing patient safety.

Keywords:
Drug–drug interactionsDrug–protein interactionsNeural networkPolypharmacy side effects prediction

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Area of Science:

  • Pharmacology and Computational Biology
  • Drug Interaction and Adverse Event Prediction

Background:

  • Polypharmacy, the concurrent use of multiple medications, presents significant patient safety challenges due to potential drug interactions.
  • Existing methods struggle to detect rare polypharmacy side effects, necessitating advanced computational approaches for prediction.
  • Understanding and mitigating these side effects is crucial for effective patient care and treatment outcomes.

Purpose of the Study:

  • To develop and evaluate a novel neural network-based method for predicting polypharmacy side effects (NNPS).
  • To introduce innovative feature vectors incorporating mono side effects and drug-protein interactions for enhanced prediction accuracy.
  • To demonstrate the efficiency and speed of the proposed method compared to established polypharmacy prediction algorithms.

Main Methods:

  • Proposed a neural network-based prediction system (NNPS) utilizing novel feature vectors.
  • Feature vectors were engineered using information on mono side effects and drug-protein interactions.
  • Employed a 5-fold cross-validation strategy over 50 iterations for robust performance assessment.

Main Results:

  • NNPS demonstrated superior performance in predicting 964 polypharmacy side effects compared to five established methods, including Decagon.
  • Achieved significant improvements in Area Under the Receiver-Operating Characteristic (9.2%), Area Under the Precision-Recall Curve (12.8%), F-score (8.6%), Accuracy (10.3%), and Matthews Correlation Coefficient (18.7%).
  • Reduced computational time from 15 days per fold (Decagon) to 8 hours, showcasing remarkable efficiency.

Conclusions:

  • The NNPS method offers a highly accurate, efficient, and computationally advantageous solution for predicting polypharmacy side effects.
  • The study highlights the potential of novel feature engineering and neural networks in addressing complex pharmacological challenges.
  • Code and datasets for the NNPS algorithm are publicly available to facilitate further research and application.