Journal ArticleDOI
Development and validation of a genetic algorithm for flexible docking.
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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.About:
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.read more
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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.
Journal ArticleDOI
Flexible ligand docking using conformational ensembles.
TL;DR: The ligand ensemble method was 100‐fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.
Journal ArticleDOI
Binding Affinity via Docking: Fact and Fiction
Tatu Pantsar,Antti Poso +1 more
TL;DR: Several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic are discussed, including the role of the solvent, the poor description of H-bonding and the lack of the systems’ true dynamics.
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DSX: A Knowledge-Based Scoring Function for the Assessment of Protein–Ligand Complexes
Gerd Neudert,Gerhard Klebe +1 more
TL;DR: The new knowledge-based scoring function DSX that consists of distance-dependent pair potentials, novel torsion angle potentialS, and newly defined solvent accessible surface-dependent potentials is introduced, featuring superior performance with respect to docking- and ranking power and runtime requirements.
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COVID-19 outbreak prediction with machine learning
Sina Ardabili,Amir Mosavi,Pedram Ghamisi,Filip Ferdinand,Annamária R. Várkonyi-Kóczy,Uwe Reuter,Timon Rabczuk,Peter M. Atkinson +7 more
TL;DR: A comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models suggests machine learning as an effective tool to model the outbreak.
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