Abstract
Hesaplamalı ilaç keşfi, geleneksel laboratuvar yöntemleri ve deneysel çalışmaların birlikte analiz edilmesini amaçlamaktadır ve ilaç keşif sürecinde önemli bir rol oynamaktadır. Bu çalışmada, hesaplamalı yöntemlerin ilaç keşfi alanında nasıl kullanıldığına odaklanılmaktadır. İlk olarak, moleküler modelleme ve simülasyon tekniklerinin, ilaç adayı bileşiklerin tasarımı ve özelliklerinin anlaşılması için nasıl kullanıldığı anlatılmaktadır. Moleküler dinamik simülasyonlar ve yapı-tabanlı ilaç tasarımı gibi yöntemler, potansiyel ilaç moleküllerinin etkileşim mekanizmalarını ve hedef proteinlerle ilişkilerini incelemektedir.
Makalenin ikinci bölümünde, sanal tarama yöntemleri ele alınmaktadır. Sanal tarama yöntemleri, hedef proteinin yapısını kullanarak, potansiyel bağlanma bölgelerini ve etkileşim alanlarını tahmin ederek, ilaç adayı moleküllerin seçiminde ve optimize edilmesinde önemli bir rol oynamaktadır. Son olarak, makalenin üçüncü bölümünde, makine öğrenmesi ve yapay zeka tekniklerinin ilaç keşfi alanında nasıl kullanıldığı tartışılmaktadır. Bu amaçla moleküler tasarım sürecinde yeni moleküllerin üretilmesinde ve ilaçların etkileşim mekanizmalarının anlaşılması incelenmiştir ve ilaç keşfi konusunda tahmin yapan bir uygulama sunulmuştur. Bu amaçla TP53 gen varyasyonlarının ilaç etkileşimleri analiz edilmiştir.
Keywords
Hesaplamalı ilaç keşfi Moleküler modelleme Makine öğrenmesi Yapı-tabanlı ilaç tasarımı Derin öğrenme Moleküler dinamik simülasyonlar
References
- Hughes, J. P.; Rees, S., Kalindjian, S. B., Philpott, K. L. 2011. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249.
- Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 18, 463–477.
- Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T. 2018. The rise of deep learning in drug discovery. Drug Discov. Today, 23, 1241–1250.
- Mater, A. C., Coote, M. L. 2019. Deep learning in chemistry. J. Chem. Inf. Model, 59, 2545–2559.
- Krizhevsky, A., Sutskever, I., Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
- Mater, A. C., Coote, M. L. 2019. Deep learning in chemistry. J. Chem. Inf. Model, 59, 2545–2559.
- Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. 2019. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 37, 1038–1040.
- Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. 2020. A deep learning approach to antibiotic discovery. Cell, 180, 688–702.
- Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 18, 463–477.
- LeCun, Y., Bengio, Y., Hinton, G. 2015. Deep learning. Nature, 521, 436–444
- Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., G´omez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R. P. 2015. Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292.
- Glen, R. C., Bender, A., Arnby, C. H., Carlsson, L., Boyer, S., Smith, J. 2006. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs, 9, 199
- Goh, G. B., Siegel, C., Vishnu, A. Hodas, N. O., Baker, N. 2017. Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert developed QSAR/QSPR models. arXiv preprint arXiv:1706.06689.
- Fernandez, M., Ban, F., Woo, G., Hsing, M., Yamazaki, T., LeBlanc, E., Rennie, P. S., Welch, W. J., Cherkasov, A. 2018. Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images. J. Chem. Inf. Model, 58, 1533–1543.
- David, L., Thakkar, A., Mercado, R., Engkvist, O. 2020. Molecular representations in AIdriven drug discovery: a review and practical guide. J. Cheminformatics, 12, 1–22.
- Gaulton, A., et al. 2011. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research. 40 (Database issue): D1100-7. doi:10.1093/nar/gkr777. PMC 3245175. PMID 21948594. Surget, S., Khoury, M.P., Bourdon, J.C. 2013. Uncovering the role of p53 splice variants in human malignancy: a clinical perspective. OncoTargets and Therapy. 7, 57–68. doi:10.2147/OTT.S53876. PMC 3872270. PMID 24379683.
- Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J. 2001. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews., 46 (1–3), 3–26. doi:10.1016/S0169-409X(00)00129-0. PMID 11259830.
- Lipinski, C.A. 2004. Lead- and drug-like compounds: the rule-offive revolution. Drug Discovery Today: Technologies., 1 (4), 337–341. doi:10.1016/j.ddtec.2004.11.007. PMID 24981612.
- Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. 2020. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov., 19, 353–364.
Abstract
Abstract: Computational Drug Discovery plays a significant role in the drug discovery process when used in conjunction with traditional laboratory methods and experimental studies. This study focuses on how computational methods are employed in the field of drug discovery. Firstly, it describes how molecular modeling and simulation techniques are utilized for the design and understanding of the properties of drug candidate compounds. Methods such as molecular dynamics simulations and structure-based drug design are commonly used to investigate the interaction mechanisms of potential drug molecules and their relationships with target proteins.
In the second section, virtual screening methods are addressed. Virtual screening methods play a crucial role in the selection and optimization of drug candidate molecules by predicting potential binding sites and interaction areas based on the structure of the target protein. Finally, machine learning and artificial intelligence techniques are discussed in the field of drug discovery. For this purpose, the generation of new molecules in the molecular design process and understanding the interaction mechanisms of drugs are examined. In this study, an application that predicts drug discovery is developed and presented. For this purpose, drug interactions of TP53 gene variations were analyzed.
Keywords
Computational drug discovery Molecular modeling Machine learning Structure-based drug design Deep learning Molecular dynamics simulations
References
- Hughes, J. P.; Rees, S., Kalindjian, S. B., Philpott, K. L. 2011. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249.
- Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 18, 463–477.
- Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T. 2018. The rise of deep learning in drug discovery. Drug Discov. Today, 23, 1241–1250.
- Mater, A. C., Coote, M. L. 2019. Deep learning in chemistry. J. Chem. Inf. Model, 59, 2545–2559.
- Krizhevsky, A., Sutskever, I., Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
- Mater, A. C., Coote, M. L. 2019. Deep learning in chemistry. J. Chem. Inf. Model, 59, 2545–2559.
- Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. 2019. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 37, 1038–1040.
- Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. 2020. A deep learning approach to antibiotic discovery. Cell, 180, 688–702.
- Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 18, 463–477.
- LeCun, Y., Bengio, Y., Hinton, G. 2015. Deep learning. Nature, 521, 436–444
- Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., G´omez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R. P. 2015. Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292.
- Glen, R. C., Bender, A., Arnby, C. H., Carlsson, L., Boyer, S., Smith, J. 2006. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs, 9, 199
- Goh, G. B., Siegel, C., Vishnu, A. Hodas, N. O., Baker, N. 2017. Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert developed QSAR/QSPR models. arXiv preprint arXiv:1706.06689.
- Fernandez, M., Ban, F., Woo, G., Hsing, M., Yamazaki, T., LeBlanc, E., Rennie, P. S., Welch, W. J., Cherkasov, A. 2018. Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images. J. Chem. Inf. Model, 58, 1533–1543.
- David, L., Thakkar, A., Mercado, R., Engkvist, O. 2020. Molecular representations in AIdriven drug discovery: a review and practical guide. J. Cheminformatics, 12, 1–22.
- Gaulton, A., et al. 2011. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research. 40 (Database issue): D1100-7. doi:10.1093/nar/gkr777. PMC 3245175. PMID 21948594. Surget, S., Khoury, M.P., Bourdon, J.C. 2013. Uncovering the role of p53 splice variants in human malignancy: a clinical perspective. OncoTargets and Therapy. 7, 57–68. doi:10.2147/OTT.S53876. PMC 3872270. PMID 24379683.
- Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J. 2001. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews., 46 (1–3), 3–26. doi:10.1016/S0169-409X(00)00129-0. PMID 11259830.
- Lipinski, C.A. 2004. Lead- and drug-like compounds: the rule-offive revolution. Drug Discovery Today: Technologies., 1 (4), 337–341. doi:10.1016/j.ddtec.2004.11.007. PMID 24981612.
- Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. 2020. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov., 19, 353–364.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | February 29, 2024 |
| Publication Date | |
| Submission Date | May 10, 2023 |
| Acceptance Date | January 22, 2024 |
| Published in Issue | Year 2024 Volume: 5 Issue: 1 |
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