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Convolutional Neural Networks for ATC Classification.

Alessandra Lumini1, Loris Nanni2

  • 1DISI, Universita di Bologna, Campus di Cesena, Via Macchiavelli, 47521 Cesena, Italy.

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|November 13, 2018
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Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for multi-label drug classification using Anatomical Therapeutic Chemical (ATC) system. The new method, based on convolutional neural networks (CNNs), significantly improves prediction accuracy for drug properties and therapeutic effects.

Keywords:
Anatomical therapeutic chemicalchemical propertiesconvolutional neural networkdeep learned featuresdrug developmentfingerprint.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Machine learning in drug discovery
  • Pharmacology and drug classification

Background:

  • The Anatomical Therapeutic Chemical (ATC) system, established by the WHO, is crucial for drug development and research.
  • Accurate ATC classification aids in deducing a compound's active ingredients, therapeutic effects, and chemical properties.
  • Predicting ATC classes is challenging due to data variability and overlapping categories, hindering traditional machine learning approaches.

Purpose of the Study:

  • To develop a robust multi-label classification system for inferring drug ATC classes.
  • To leverage deep learning, specifically Convolutional Neural Networks (CNNs), for enhanced feature extraction from compound representations.
  • To improve the accuracy and reliability of automatic drug classification for research and development.

Main Methods:

  • A 2D representation of compounds was created by extracting chemical-chemical interaction, structural, and fingerprint similarities.
  • A Convolutional Neural Network (CNN) was employed as a deep feature extractor on the 2D compound matrices.
  • Two general multi-label classifiers were trained on deep learned features, with results fused using an average rule.

Main Results:

  • The proposed CNN-based method demonstrated superior prediction quality compared to existing state-of-the-art approaches.
  • Rigorous cross-validation confirmed the enhanced performance across multiple evaluation metrics.
  • The predictor showed significant improvements, particularly in 'absolute true' and 'absolute false' rates, crucial for multi-label systems.

Conclusions:

  • The developed deep learning model offers a significant advancement in automatic ATC classification.
  • The CNN-based feature extraction effectively addresses the complexities of multi-label drug classification.
  • The method's superior performance provides a valuable tool for drug discovery and pharmacological research.