Setiawan, Hartanto (2017) Kajian Penempatan Ligan Pada Protein Menggunakan Pendekatan Algoritma Genetika. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penempatan ligan pada protein atau molecule docking merupakan bidang komputasi yang sedang berkembang. Metode molecular docking adalah metode yang bermanfaat untuk mencari kombinasi interaksi protein dan ligan serta menjadi dasar penemuan obat secara simulasi. Molecular docking yang digunakan adalah flexible docking dan jenis protein-ligand docking. Pendekatan algoritma genetika merupakan metode alternatif yang bisa digunakan untuk simulasi molecular docking. Hasil dari pendekatan algoritma genetika yaitu berupa penempatan posisi docking yang optimum. Penerapan algoritma genetika dalam docking tidak berlaku untuk semua protein dan ligan. Dalam penerapannya tingkat homologi mempengaruhi keberhasilan dari docking.
================================================================================================================== The placement of the ligand on a docking protein molecules or liquid computing is being developed. Molecular docking methods are methods that are useful for creating a combination of protein and ligand interactions as well as the basis of drug discovery in the simulation. Molecular docking that is used in is a flexible docking and the type of protein-ligand docking. Genetic algorithm approach to illiquid alternative methods that can be used to define the molecular docking simulations. The result of the genetic algorithm approach applies in the form of an optimal docking position placement. Application of genetic algorithm in the docking does not apply to all the protein and Ligand. In its application-level homology affect the success of the Dock
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSMa 519.625 Set k |
| Uncontrolled Keywords: | Algoritma Genetika; Molecular docking; Protein-ligand docking; Flexible docking; Genetic Algorithm; Molecular docking; Protein-ligand docking; Flexible docking |
| Subjects: | Q Science > QA Mathematics > QA402.5 Genetic algorithms. Interior-point methods. Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Hartanto Setiawan . |
| Date Deposited: | 23 Feb 2018 04:09 |
| Last Modified: | 19 Jun 2020 03:32 |
| URI: | http://repository.its.ac.id/id/eprint/46281 |
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