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Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network

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Abstract

The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fields of medicinal chemistry and drug industries have been a profound scientific development, since only a few amounts of the small molecule libraries were identified as approvable drugs. In this study, a deep belief network was utilized to construct a druglikeness classification model. For this purpose, small molecules and approved drugs from the ZINC database were selected for the unsupervised pre-training step and supervised training step. Various binary fingerprints such as Macc 166 bit, PubChem 881 bit, and Morgan 2048 bit as data features were investigated. The report revealed that using an unsupervised pre-training phase can lead to a good performance model and generalizability capability. Accuracy, precision, and recall of the model for Macc features were 97%, 96%, and 99%, respectively. For more consideration about the generalizability of the model, the external data by expression and investigational drugs in drug banks as drug data and randomly selected data from the ZINC database as non-drug were created. The results confirmed the good performance and generalizability capability of the model. Also, the outcomes depicted that a large proportion of misclassified non-drug small molecules ascertain the bioavailability conditions and could be investigated as a drug in the future. Furthermore, our model attempted to tap potential opportunities as a drug filter in drug discovery.

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Authors and Affiliations

  1. Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran

    Seyed Aghil Hooshmand & Ali Masoudi-Nejad

  2. Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Seyed Aghil Hooshmand & Ali Masoudi-Nejad

  3. Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Tehran, Iran

    Sadegh Azimzadeh Jamalkandi

  4. Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran

    Seyed Mehdi Alavi

Authors
  1. Seyed Aghil Hooshmand
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  2. Sadegh Azimzadeh Jamalkandi
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  3. Seyed Mehdi Alavi
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  4. Ali Masoudi-Nejad
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Correspondence to Ali Masoudi-Nejad.

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Hooshmand, S.A., Jamalkandi, S.A., Alavi, S.M. et al. Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network. Mol Divers 25, 827–838 (2021). https://doi.org/10.1007/s11030-020-10065-7

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