In silico Maturation: Processing Sequences to Improve Biopolymer Functions Based on Genetic Algorithms

2014 ◽  
pp. 271-288 ◽  
Author(s):  
Nasa Savory ◽  
Koichi Abe ◽  
Wataru Yoshida ◽  
Kazunori Ikebukuro
Keyword(s):  
Genetic Algorithms ◽  
In Silico

Feature Selection Methods Based on Genetic Algorithms for in Silico Drug Design

2003 ◽  
pp. 317-339 ◽  
Author(s):  
Mark J. Embrechts ◽  
Muhsin Ozdemir ◽  
Larry Lockwood ◽  
Curt Breneman ◽  
Kristin Bennett ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Drug Design ◽  
In Silico ◽  
Selection Methods ◽  
In Silico Drug Design

scholarly journals A machine learning model for the prediction of drug permeability across the Blood-Brain Barrier: a comparative approach

2020 ◽  
Author(s):  
Ralph Saber ◽  
Rami Mhanna ◽  
Sandy Rihana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Blood Brain Barrier ◽  
In Silico ◽  
Molecular Descriptors ◽  
Brain Barrier ◽  
Drug Permeability ◽  
Blood Brain ◽  
Sequential Feature Selection

Abstract Background: Drug permeability across the blood-brain barrier (BBB) is a critical challenge for successful drug discovery which has led to multiple efforts to develop in silico predictive models. Most of the in silico models are based on the molecular descriptors of the drugs. In this work, we compare the ability of sequential feature selection and genetic algorithms in selecting the most relevant descriptors and hence enhancing the permeability prediction accuracy.Methods: Five different classifiers were initially trained on a dataset using eight molecular descriptors. Then, sequential feature selection and genetic algorithms were performed separately and the same classifiers were trained using the descriptors chosen by each algorithm.Results: The highest overall accuracy obtained without feature selection was 94.98%. This accuracy increased with sequential feature selection and genetic algorithms on multiple classifiers. However, the highest accuracy (96.23%) was obtained after performing genetic algorithm on the feature vector. Moreover, genetic algorithm with a fitness function based on the performance of a support vector machine led to an increase in the accuracy of all the tested classifiers unlike sequential feature selection.Conclusions: The findings show that genetic algorithm is a more robust approach than sequential feature selection in choosing the most relevant molecular descriptors involved in the permeability across the blood-brain barrier. The results also highlight the importance of the polar surface area of drugs in crossing the BBB.

SIMULATED ANNEALING AND OPTIMAL PROTOCOLS

2009 ◽  
Vol 18 (08) ◽  
pp. 1565-1579 ◽  
Author(s):  
MARZIO PENNISI ◽  
ROBERTO CATANUTO ◽  
FRANCESCO PAPPALARDO ◽  
SANTO MOTTA ◽  
EMILIO MASTRIANI ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
In Silico ◽  
High Performance ◽  
Optimization Procedure ◽  
Vaccination Schedule ◽  
In Vivo Experiments ◽  
Prophylactic Vaccination ◽  
Performance Computing

Many human tumors cannot easily be avoided. In most cases a prophylactic vaccination prevents the tumor growth. In particular the Triplex vaccine prevented mammary carcinoma formation using a Chronic schedule, but it is not known if this schedule is minimal. A computational model named SimTriplex was able to reproduce in silico the in vivo experiments. The combination of SimTriplex and Genetic Algorithms (GA), produced a minimal vaccination schedule capable to guarantee in silico survival. This process required the use of a High Performance Computing (HPC) infrastructure for several days. In this paper we show another optimization procedure based on simulated annealing approach used to get a faster algorithm response.

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