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


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.


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

List of abbreviations


Absorption, distribution, metabolism, excretion and toxicity


Alzheimer’s disease

log S

Aqueous solubility


Artificial neural networks


Bayesian-regularized artificial neural networks


Bayesian-regularized genetic neural networks


Blood–brain barrier


Comparative molecular field analysis


Conjugated Gradient


Genetic algorithm


Genetic algorithm-based partial least squares


Genetic algorithm-optimized support vector machines


Genetic neural networks


Genetic stochastic resonance


Human intestinal absorption


Human plasma protein binding rate

Log P



Luteinizing hormone-releasing hormone


Matrix metalloproteinase


Mitochondrial toxicity


Multiple linear regression


Negative mitochondrial toxicity


Neural network ensembles


Normal coordinate eigenvalue


Oral bioavailability


Partial least squares




Physicochemical composition


Positive mitochondrial toxicity


Principal component-genetic algorithm-artificial neural network


Principal components


Projection pursuit regression


Quantitative structure–activity relationship


Quantitative structure–property relationship


Radial Basic Function


Self-organized maps


Stochastic resonance


Support vector machines


Thyroid hormone receptor b1


Torsades de pointes


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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