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Molecular Diversity

, Volume 15, Issue 1, pp 269–289 | Cite as

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

  • Michael FernandezEmail author
  • Julio Caballero
  • Leyden Fernandez
  • Akinori Sarai
Comprehensive Review

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.

Keywords

Drug design Enzyme inhibition Feature selection In silico modeling QSAR Review SAR Structure–activity relationships 

List of 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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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Michael Fernandez
    • 1
    Email author
  • Julio Caballero
    • 2
  • Leyden Fernandez
    • 3
  • Akinori Sarai
    • 1
  1. 1.Department of Bioscience and BioinformaticsKyushu Institute of Technology (KIT)IizukaJapan
  2. 2.Centro de Bioinformatica y Simulacion MolecularUniversidad de TalcaTalcaChile
  3. 3.Barcelona Supercomputing Center—Centro Nacional de SupercomputaciónBarcelonaSpain

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