Journal ArticleDOI
Genetic algorithms in molecular recognition and design
TLDR
A genetic algorithm takes an initial set of possible starting solutions, and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution.About:
This article is published in Trends in Biotechnology.The article was published on 1995-12-01. It has received 120 citations till now. The article focuses on the topics: Crossover & Genetic algorithm.read more
Citations
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Journal ArticleDOI
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Garrett M. Morris,David S. Goodsell,Robert Scott Halliday,Ruth Huey,William E. Hart,Richard K. Belew,Arthur J. Olson +6 more
TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
Journal ArticleDOI
Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
TL;DR: Artificial neural networks are biologically inspired computer programs designed to simulate the way in which the human brain processes information and represent a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes.
Journal ArticleDOI
Virtual screening : an overview
TL;DR: This review presents the current state of the art in virtual screening and discusses approaches that will allow the evaluation of larger numbers of compounds.
Book ChapterDOI
A Gentle Introduction to Memetic Algorithms
Pablo Moscato,Carlos Cotta +1 more
TL;DR: The generic denomination of ‘Memetic Algorithms’ (MAs) is used to encompass a broad class of metaheuristics (i.e. general purpose methods aimed to guide an underlying heuristic) and proved to be of practical success in a variety of problem domains and in particular for the approximate solution of NP Optimization problems.
Journal ArticleDOI
Low-resolution structures of proteins in solution retrieved from X-ray scattering with a genetic algorithm.
TL;DR: Small-angle x-ray solution scattering (SAXS) is analyzed with a new method to retrieve convergent model structures that fit the scattering profiles, and the low-resolution solution structure of lysozyme has been directly modeled from its experimental SAXS profile.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Related Papers (5)
Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships
David Rogers,Anton J. Hopfinger +1 more