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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
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Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction.

Hamza Hentabli1,2, Billel Bengherbia1, Faisal Saeed2,3

  • 1Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria.

International Journal of Molecular Sciences
|November 11, 2022
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Summary
This summary is machine-generated.

This study introduces a novel deep learning method using a convolutional neural network (CNN) and a Mol2mat molecular matrix representation to predict chemical compound bioactivity. The approach significantly improves prediction accuracy compared to existing machine learning algorithms.

Keywords:
activity prediction modelbioactive moleculesbiological activitiesconvolutional neural networkdeep learning

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning in drug discovery

Background:

  • Predicting molecular behavior and properties is crucial for various scientific processes.
  • Current computational methods like machine learning have limitations in accuracy and error rates for predicting physicochemical properties.

Purpose of the Study:

  • To develop a novel deep learning technique for accurate prediction of chemical compound bioactivity.
  • To introduce and evaluate a new molecular matrix representation called Mol2mat.

Main Methods:

  • Developed a deep learning convolutional neural network (CNN) model.
  • Utilized a novel Mol2mat molecular matrix representation derived from 2D-fingerprint descriptors.
  • Experimented with standard datasets (MDDR, Sutherland) and analyzed combinations of eight fingerprints, focusing on the five best descriptors.

Main Results:

  • The proposed CNN method combined with Mol2mat representation achieved a high performance of 98% AUC.
  • A specific combination of three fingerprints (ECFP4, EPFP4, and ECFC4) yielded the best results.
  • Outperformed state-of-the-art machine learning algorithms including NaiveB, LSVM, and RBFN.

Conclusions:

  • The novel deep learning approach with Mol2mat representation offers a significant advancement in predicting chemical bioactivity.
  • This method demonstrates superior accuracy and reduced error rates compared to traditional machine learning algorithms.
  • Highlights the potential of deep learning and novel molecular representations in cheminformatics and drug discovery.