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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Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search
TL;DR: The Surflex flexible molecular docking method has been generalized and extended in two primary areas related to the search component of docking: incorporation of a small-molecule force-field and knowledge of well established molecular interactions between ligand fragments and a target protein can be directly exploited to guide the search process.
Journal ArticleDOI
Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance
Michael J. Hartshorn,Marcel L. Verdonk,Gianni Chessari,Suzanne C. Brewerton,Wijnand T. M. Mooij,Paul N. Mortenson,Christopher William Murray +6 more
TL;DR: A procedure for analyzing and classifying publicly available crystal structures has been developed and has been used to identify high-resolution protein-ligand complexes that can be assessed by reconstructing the electron density for the ligand using the deposited structure factors.
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GEMDOCK: A generic evolutionary method for molecular docking
Jinn-Moon Yang,Chun Chen Chen +1 more
TL;DR: GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect.
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KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
TL;DR: This work proposes here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compares this approach to other machine- learning and scoring methods using several diverse data sets.
Journal ArticleDOI
Evaluation of docking performance: comparative data on docking algorithms.
TL;DR: This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate, and a correspondence between the nature of the active site and the best docking algorithm can be found.
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