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Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

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Abstract

Many articles in “in silico” drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure–activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand–target interactions.

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Abbreviations

ADMET:

Absorption, distribution, metabolism, excretion and toxicity

AD:

Alzheimer’s disease

log S :

Aqueous solubility

ANNs:

Artificial neural networks

BRANNs:

Bayesian-regularized artificial neural networks

BRGNNs:

Bayesian-regularized genetic neural networks

BBB:

Blood–brain barrier

CoMFA:

Comparative molecular field analysis

CG:

Conjugated Gradient

GA:

Genetic algorithm

GA-PLS:

Genetic algorithm-based partial least squares

GA-SVM:

Genetic algorithm-optimized support vector machines

GNN:

Genetic neural networks

GSR:

Genetic stochastic resonance

HIA:

Human intestinal absorption

PPBR:

Human plasma protein binding rate

Log P :

Lipophilicity

LHRH:

Luteinizing hormone-releasing hormone

MMP:

Matrix metalloproteinase

MT:

Mitochondrial toxicity

MLR:

Multiple linear regression

MT-:

Negative mitochondrial toxicity

NNEs:

Neural network ensembles

EVA:

Normal coordinate eigenvalue

BIO:

Oral bioavailability

PLS:

Partial least squares

P-gp:

P-glycoprotein

PCC:

Physicochemical composition

MT+:

Positive mitochondrial toxicity

PC-GA-ANN:

Principal component-genetic algorithm-artificial neural network

PCs:

Principal components

PPR:

Projection pursuit regression

QSAR:

Quantitative structure–activity relationship

QSPR:

Quantitative structure–property relationship

RBF:

Radial Basic Function

SOMs:

Self-organized maps

SR:

Stochastic resonance

SVMs:

Support vector machines

Trb1:

Thyroid hormone receptor b1

Tdp:

Torsades de pointes

VKCs:

Voltage-gated potassium channels

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Authors and Affiliations

  1. Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502, Japan

    Michael Fernandez & Akinori Sarai

  2. Centro de Bioinformatica y Simulacion Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile

    Julio Caballero

  3. Barcelona Supercomputing Center—Centro Nacional de Supercomputación, Nexus II Building c/ Jordi Girona, 29, 08034, Barcelona, Spain

    Leyden Fernandez

Authors
  1. Michael Fernandez
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  2. Julio Caballero
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  3. Leyden Fernandez
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  4. Akinori Sarai
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Correspondence to Michael Fernandez.

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Fernandez, M., Caballero, J., Fernandez, L. et al. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers 15, 269–289 (2011). https://doi.org/10.1007/s11030-010-9234-9

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  • DOI: https://doi.org/10.1007/s11030-010-9234-9

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