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Classifying Calpain Inhibitors for the Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

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Abstract

Calpain inhibitors are possible therapeutic agents in the treatment of cataracts. These covalent inhibitors contain an electrophilic anchor (“warhead”), an aldehyde that reacts with the active site cysteine. Whilst high throughput docking of such ligands into high resolution protein structures (e.g. calpain) is a standard computational approach in drug discovery, there is no docking program that consistently achieves low rates of both false positives (FPs) and negatives (FNs) for ligands that react covalently (via irreversible interactions) with the target protein. Schroedinger’s GLIDE score, widely used to screen ligand libraries, is known to give high false classification, however a two-level Self Organizing Map (SOM) artificial neural network (ANN) algorithm, with KM clustering proved that the addition of two structural components of the calpain molecule, number hydrogen bonds and warhead distance, combined with GLIDE score (or its partial energy subcomponents) provide a superior predictor set for classification of true molecular binding strength (IC50). SOM ANN/KM significantly reduced the number of FNs by 64 % and FPs by 26 %, compared to the glide score alone. FPs were shown to be mostly esters and amides plus alcohols and non-classical, and FNs mainly aldehydes and ketones, masked aldehydes and ketones and Michael.

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Authors and Affiliations

  1. School of Mathematical and Physical Sciences, The University of Newcastle, NSW, Australia

    I. L. Hudson

  2. Robinson Research Institute, The University of Adelaide, Adelaide, SA, 5005, Australia

    S. Y. Leemaqz

  3. Institute of Biomaterial Science, Helmholtz-Zentrum Geesthacht, Teltow, Germany

    A. T. Neffe

  4. School of Physics and Chemistry, The University of Adelaide, Adelaide, SA, 5005, Australia

    A. D. Abell

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  1. I. L. Hudson
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  3. A. T. Neffe
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  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Hudson, I.L., Leemaqz, S.Y., Neffe, A.T., Abell, A.D. (2016). Classifying Calpain Inhibitors for the Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_9

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