Researcher:
Rahim, Fatih

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PhD Student

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Fatih

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Rahim

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Rahim, Fatih

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2016 - 201912020 - 20211

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    PublicationOpen Access
    Structure-based design and classifications of small molecules regulating the circadian rhythm period
    (Nature Portfolio, 2021) Yılmaz, Fatma; Öztürk, Nuri; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Molecular Biology and Genetics; Türkay, Metin; Rahim, Fatih; Gül, Şeref; Kavaklı, İbrahim Halil; Işın, Şafak; Faculty Member; Researcher; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Molecular Biology and Genetics; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/A; 40319; N/A
    Circadian rhythm is an important mechanism that controls behavior and biochemical events based on 24 h rhythmicity. Ample evidence indicates disturbance of this mechanism is associated with different diseases such as cancer, mood disorders, and familial delayed phase sleep disorder. Therefore, drug discovery studies have been initiated using high throughput screening. Recently the crystal structures of core clock proteins (CLOCK/BMAL1, Cryptochromes (CRY), Periods), responsible for generating circadian rhythm, have been solved. Availability of structures makes amenable core clock proteins to design molecules regulating their activity by using in silico approaches. In addition to that, the implementation of classification features of molecules based on their toxicity and activity will improve the accuracy of the drug discovery process. Here, we identified 171 molecules that target functional domains of a core clock protein, CRY1, using structure-based drug design methods. We experimentally determined that 115 molecules were nontoxic, and 21 molecules significantly lengthened the period of circadian rhythm in U2OS cells. We then performed a machine learning study to classify these molecules for identifying features that make them toxic and lengthen the circadian period. Decision tree classifiers (DTC) identified 13 molecular descriptors, which predict the toxicity of molecules with a mean accuracy of 79.53% using tenfold cross-validation. Gradient boosting classifiers (XGBC) identified 10 molecular descriptors that predict and increase in the circadian period length with a mean accuracy of 86.56% with tenfold cross-validation. Our results suggested that these features can be used in QSAR studies to design novel nontoxic molecules that exhibit period lengthening activity.
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    PublicationOpen Access
    Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6
    (EDP Sciences, 2016) Department of Industrial Engineering; N/A; Department of Chemical and Biological Engineering; N/A; Tardu, Mehmet; Rahim, Fatih; Kavaklı, İbrahim Halil; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 40319; 24956
    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.