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Journal ArticleDOI

Development and validation of a genetic algorithm for flexible docking.

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TLDR
GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.
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This article is published in Journal of Molecular Biology.The article was published on 1997-04-04. It has received 5882 citations till now. The article focuses on the topics: Searching the conformational space for docking & Protein–ligand docking.

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Machine learning in computational docking

TL;DR: The paradigm shift is presented elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area, with open questions and potential future research directions that can be pursued.
Journal ArticleDOI

Evaluation of library ranking efficacy in virtual screening

TL;DR: It is shown that the scoring functions rank the observed binding modes higher than the inaccurate poses provided that the experimental poses are available, and suggests that the number of false positives can be lowered with conformers closer to bioactive ones.
Journal ArticleDOI

Molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches.

TL;DR: This study clearly demonstrates that the MM-PBSA based ΔG(Bind) (ΔG( Bind)-PB) values provide very good correlation with experimental IC(50) values (quantitative and qualitative) when MD simulation is carried out for a longer time; however, MM-GBSA basedΓ(Bind)-GB values show acceptable correlation for shorter time of simulation also.
Journal ArticleDOI

Assessing search strategies for flexible docking

TL;DR: This work assesses the efficiency of molecular dynamics, Monte Carlo, and genetic algorithms for docking five representative ligand–receptor complexes and finds that MD is most efficient in the case of the large search space, and GA outperforms the other methods in the small search space.
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

Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book

Molecular theory of gases and liquids

TL;DR: Molecular theory of gases and liquids as mentioned in this paper, molecular theory of gas and liquids, Molecular theory of liquid and gas, molecular theories of gases, and liquid theory of liquids, مرکز
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