Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6
| Autor: | I. Halil Kavakli, Mehmet Tardu, Metin Turkay, Fatih Rahim |
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| Přispěvatelé: | Tardu, Mehmet, Rahim, Fatih, Kavaklı, İbrahim H. (ORCID 0000-0001-6624-3505 & YÖK ID 40319), Türkay, Metin (ORCID 0000-0003-4769-6714 & YÖK ID 24956), Graduate School of Sciences and Engineering, College of Engineering, Department of Industrial Engineering, Department of Industrial Engineering and Operations Management, Department of Chemical and Biological Engineering, Department of Computational Sciences and Engineering |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Drug Computer science media_common.quotation_subject Management Science and Operations Research Machine learning computer.software_genre 01 natural sciences Theoretical Computer Science 03 medical and health sciences Molecular descriptor 0103 physical sciences Partial least squares regression media_common Structure-based drug design SIRT6 MILP-HB Virtual screening 010304 chemical physics business.industry Drug discovery Small molecule Computer Science Applications 030104 developmental biology A priori and a posteriori Structure based Artificial intelligence Multidisciplinary sciences Operations research and management science business computer |
| Zdroj: | RAIRO, Operations Research |
| Popis: | Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low-and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature. NA |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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