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Neural Network Ensemble Based QSAR Model for the BBB Challenge: A Review

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Transactions on Engineering Technologies

Abstract

The blood-brain barrier (BBB) presents a real challenge to the pharmaceutical industry. The BBB is a very effective screener of diverse kinds of bacterial infections. Unfortunately, this functionality prevents from many drugs to penetrate it. In order to improve drug development process an assessment model is required. Effective assessment models can drastically reduce development times, by cutting off drugs with low success rates. It also saves financial resources since clinical trials will focus mainly on drugs with higher likelihood of permeation. This work addresses the challenge by means of artificial neural net (ANN) based assessment tool. Embedding ‘wisdom of experts’ approach, the presented assessment tool is combined of a neural net ensemble, a group of trained neural nets that correspond to an input value set with a prediction of the barrier permeation. The returned output is the median of the ensemble’s members output. The input set is composed of drug physicochemical properties such: Lipophilicity, Molecular Size (depends on Molecular Mass/Weight), Plasma Protein Binding, PSA—Polar Surface Area of a molecule, and Vd—Volume of Distribution, and Plasma Half Life (t ½). Challenged with the relatively small learning data-set, leave one out (LOO) which is a special case of k-fold cross validation is conducted. Although the training effort for building ANNs is much higher, in small data-sets ANNs yield much better model fitting and prediction results than the logistic regression.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, ORT Braude College, Snunit 51, P.O.Box 78, Karmiel, 21982, Israel

    Mati Golani

  2. Department of Biotechnology Engineering, ORT Braude College, Snunit 51, P.O.Box 78, Karmiel, 21982, Israel

    Idit. I. Golani

Authors
  1. Mati Golani
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  2. Idit. I. Golani
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Correspondence to Mati Golani .

Editor information

Editors and Affiliations

  1. Computer & Communication, Catholic University of DaeGu Engineering College, DaeGu, Korea, Republic of (South Korea)

    Haeng Kon Kim

  2. College of Engineering, California State Polytechnic University, Pomona, California, USA

    Mahyar A. Amouzegar

  3. IAENG Secretariat, International Association of Engineers, Hong Kong, Hong Kong SAR

    Sio-long Ao

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Golani, M., Golani, I.I. (2015). Neural Network Ensemble Based QSAR Model for the BBB Challenge: A Review. In: Kim, H., Amouzegar, M., Ao, Sl. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7236-5_5

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  • DOI: https://doi.org/10.1007/978-94-017-7236-5_5

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-7235-8

  • Online ISBN: 978-94-017-7236-5

  • eBook Packages: EngineeringEngineering (R0)

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