Molecular docking with multi-objective Particle Swarm Optimization

Janson, Stefan; Merkle, Daniel; Middendorf, Martin

doi:10.1016/j.asoc.2007.05.005

Cited by 105 publications

(62 citation statements)

References 12 publications

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“…[19,20,23,[35][36][37] It has been reported that better optimization of the AutoDock energy leads to more accurate docking predictions. [15][16][17]28] However, we observe that although CSA can sample conformations with lower energy than the Lamarckian genetic algorithm (LGA) [4] of AutoDock, the lower energy conformations tend to be less native-like. Modification of the AutoDock energy by addition of an explicit torsional energy term from the piecewise linear potential (PLP) score [38] resulted in better performance due to relief of torsional strains.…”

Section: Introductionmentioning

confidence: 96%

LigDockCSA: Protein–ligand docking using conformational space annealing

et al. 2011

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Protein-ligand docking techniques are one of the essential tools for structure-based drug design. Two major components of a successful docking program are an efficient search method and an accurate scoring function. In this work, a new docking method called LigDockCSA is developed by using a powerful global optimization technique, conformational space annealing (CSA), and a scoring function that combines the AutoDock energy and the piecewise linear potential (PLP) torsion energy. It is shown that the CSA search method can find lower energy binding poses than the Lamarckian genetic algorithm of AutoDock. However, lower-energy solutions CSA produced with the AutoDock energy were often less native-like. The loophole in the AutoDock energy was fixed by adding a torsional energy term, and the CSA search on the refined energy function is shown to improve the docking performance. The performance of LigDockCSA was tested on the Astex diverse set which consists of 85 protein-ligand complexes. LigDockCSA finds the best scoring poses within 2 Å root-mean-square deviation (RMSD) from the native structures for 84.7% of the test cases, compared to 81.7% for AutoDock and 80.5% for GOLD. The results improve further to 89.4% by incorporating the conformational entropy.

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Section: Introductionmentioning

confidence: 96%

LigDockCSA: Protein–ligand docking using conformational space annealing

et al. 2011

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“…The swarm intelligence models have an advantage over simulated annealing and genetic algorithm approaches when the environment may change dynamically (Janson, 2008). They are computationally expensive, which could prevent the simulation from running in "real time".…”

Section: Background Multi-agent Systemsmentioning

confidence: 99%

SimCenter Hawaii: Virtual Reality Applications for Health Care Education and Training

Burgess¹

2008

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 5f. WORK UNIT NUMBER PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 17-12-2008 REPORT TYPE Final DATES COVERED (From -To PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORTUCERA 651 Ilalo Street, MEB 224N Honolulu, HI 96813 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)U.S. Army Medical Research Fort Detrick, MD 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S)12. DISTRIBUTION / AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES N/A 14. ABSTRACT Technology-Enabled Learning and Intervention Systems (TELIS) describes a development model and associated tools for advanced educational products and applications. As an extension of this roadmap, SimCenter Hawaii has developed a research design model that addresses specific challenges (e.g., improved user interfaces, the development of telecollaboration tools) directed toward improving the development of effective TELIS educational applications that can benefit the military's training programs. The following 3 medical simulation projects are herein proposed under the SimCenter Hawaii's TELIS model: 1) Implementing a combination of usability engineering methods, an evaluation model that combines two iterations of heuristic usability evaluations and one iteration of user testing, will achieve measurable software design improvements in development of the VR nephron; 2) Didactic training plus VR Triage training will result in improved triage skills acquisition over didactic training alone; and 3) the Virtual Reality Motor-Skills Simulator will result in equal or better results than a box trainer in developing fine-motor surgical skills, as assessed by performance of a virtual procedure on the LapSim simulator. SUBJECT TERMS IntroductionCurrently, technology has evolved to potentially transform learning systems, however; to achieve effective learning tools, great effort must be taken to develop, test and disseminate the technology.(3) A systematic, reproducible approach to research and development must be implemented so that results produce usable, validated, learning systems. SimC...

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“…Researchers utilized a metaheuristic calculation based on multi-criteria unbiased optimization as a way to deal with high frequency trading software based on two parameters, to achieve specific benefit proportion and change of benefit. The software was tested by executing trades on a dummy business sector test platform [5]. Utilizing this approach, the software eventually developed a few trading strategies which were superior, at least in terms of the narrowly defined success parameters, to the strategies deployed by unassisted human traders.…”

Section: Introductionmentioning

confidence: 99%

A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms

Tan¹,

Wang²,

Xiong³

2017

Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)

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There are two types of modern portfolio theory: narrow and broad. A narrowly conceived portfolio theory is often referred to by the name Markowitz portfolio theory. Often generalized portfolio theory, in addition to classical portfolio theory, and various alternative portfolio theories, include the capital asset pricing model and an effective securities market theory of capital markets. At the same time, because the traditional EMH cannot explain market anomalies, the various portfolio theories are have been challenged by behavioral financial theory. This study uses pattern recognition algorithms to improve the traditional portfolio optimization models. By utilizing improved particle swarm optimization algorithms, a hybrid optimization model based on this would be the result of the experiment, compared with that based on the traditional neural network model for quantitative model effect. Particle swarm optimization algorithms were originally generated to produce graphical simulations of flocks of birds and other such unpredictable movements. The developmental basis of the algorithms used in this experiment are observations of animal social behavior, demonstrating that sharing of information society provides groups with an evolutionary advantage. By joining near the speed of matching, taking multidimensional search into account, and according to speeds and distances, we can thus form the original version of particle swarm optimization. After introducing the parameters of inertia weight to better control the development of swarm behavior, we have produced the standard version. This study will focus on analysis to improve the process of generating particle swarm optimization models.

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Molecular docking with multi-objective Particle Swarm Optimization

Cited by 105 publications

References 12 publications

LigDockCSA: Protein–ligand docking using conformational space annealing

LigDockCSA: Protein–ligand docking using conformational space annealing

SimCenter Hawaii: Virtual Reality Applications for Health Care Education and Training

A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms

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