RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo

: figuras, tablas

Autores:: Jaramillo Villegas, José Alfredo
Rosero Arias, Cristian

Tipo de recurso:: Book

Fecha de publicación:: 2024

Institución:: Universidad Tecnológica de Pereira

Repositorio:: Repositorio Institucional UTP

Idioma:: spa

id	UTP2_6cd220b4f2b562605843383af466a635
oai_identifier_str	oai:repositorio.utp.edu.co:11059/15513
network_acronym_str	UTP2
network_name_str	Repositorio Institucional UTP
repository_id_str
dc.title.spa.fl_str_mv	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
title	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
spellingShingle	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo 370 - Educación Inteligencia artificial Aprendizaje automático (Inteligencia artificial) Biotecnología Inteligencia artificial Aprendizaje automático Bioinformática Biología computacional Biotecnología Teoría de grafos Farmacología
title_short	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
title_full	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
title_fullStr	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
title_full_unstemmed	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
title_sort	RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo
dc.creator.fl_str_mv	Jaramillo Villegas, José Alfredo Rosero Arias, Cristian
dc.contributor.author.none.fl_str_mv	Jaramillo Villegas, José Alfredo Rosero Arias, Cristian
dc.subject.ddc.none.fl_str_mv	370 - Educación
topic	370 - Educación Inteligencia artificial Aprendizaje automático (Inteligencia artificial) Biotecnología Inteligencia artificial Aprendizaje automático Bioinformática Biología computacional Biotecnología Teoría de grafos Farmacología
dc.subject.armarc.none.fl_str_mv	Inteligencia artificial Aprendizaje automático (Inteligencia artificial) Biotecnología
dc.subject.proposal.spa.fl_str_mv	Inteligencia artificial Aprendizaje automático Bioinformática Biología computacional Biotecnología Teoría de grafos Farmacología
description	: figuras, tablas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-11-27T16:55:21Z
dc.date.available.none.fl_str_mv	2024-11-27T16:55:21Z
dc.date.issued.none.fl_str_mv	2024
dc.type.none.fl_str_mv	Libro
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_2f33
dc.type.content.none.fl_str_mv	Text
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/book
format	http://purl.org/coar/resource_type/c_2f33
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/11059/15513
dc.identifier.eisbn.none.fl_str_mv	ISBN: 978-958-722-954-7
dc.identifier.doi.none.fl_str_mv	https://doi.org/10.22517/9789587229547.
dc.identifier.instname.none.fl_str_mv	Universidad Tecnológica de Pereira
dc.identifier.reponame.none.fl_str_mv	Repositorio Universidad Tecnológica de Pereira
dc.identifier.repourl.none.fl_str_mv	https://repositorio.utp.edu.co/home
url	https://hdl.handle.net/11059/15513 https://doi.org/10.22517/9789587229547. https://repositorio.utp.edu.co/home
identifier_str_mv	ISBN: 978-958-722-954-7 Universidad Tecnológica de Pereira Repositorio Universidad Tecnológica de Pereira
dc.language.iso.none.fl_str_mv	spa
language	spa
dc.relation.references.none.fl_str_mv	[1] J.-F. Wang, D.-Q. Wei y K.-C. Chou, “Pharmacogenomics and personalized use of drugs,” Current topics in medicinal chemistry, vol. 8, n.o 18, págs. 1573-1579, 2008. [2] Instituto Nacional de Salud, Observatorio Nacional de Salud, “COVID-19: pro- greso de la pandemia y sus desigualdades en Colombia; Décimo tercero Informe Técnico,” Instituto Nacional de Salud, inf. téc., 2021, ISSN: 2346-3325. [3] T. He, M. Heidemeyer, F. Ban, A. Cherkasov y M. Ester, “SimBoost: a read- across approach for predicting drug–target binding affinities using gradient boos- ting machines,” Journal of cheminformatics, vol. 9, n.o 1, págs. 1-14, 2017. [4] J. A. DiMasi, H. G. Grabowski y R. W. Hansen, “Innovation in the pharma- ceutical industry: new estimates of R&D costs,” Journal of health economics, vol. 47, págs. 20-33, 2016. [5] Z. Wu, B. Ramsundar, E. N. Feinberg y col., “MoleculeNet: a benchmark for molecular machine learning,” Chemical science, vol. 9, n.o 2, págs. 513-530, 2018. [6] J. Li, A. Fu y L. Zhang, “An overview of scoring functions used for protein–ligand interactions in molecular docking,” Interdisciplinary Sciences: Computational Life Sciences, vol. 11, n.o 2, págs. 320-328, 2019. [7] D. R. Koes, M. P. Baumgartner y C. J. Camacho, “Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise,” Journal of chemical information and modeling, vol. 53, n.o 8, págs. 1893-1904, 2013. [8] N. M. Hassan, A. A. Alhossary, Y. Mu y C.-K. Kwoh, “Protein-ligand blind doc- king using QuickVina-W with inter-process spatio-temporal integration,” Scien- tific reports, vol. 7, n.o 1, págs. 1-13, 2017. [9] J. C. Pereira, E. R. Caffarena y C. N. Dos Santos, “Boosting docking-based virtual screening with deep learning,” Journal of chemical information and mo- deling, vol. 56, n.o 12, págs. 2495-2506, 2016. [10] Z. Liao, R. You, X. Huang, X. Yao, T. Huang y S. Zhu, “DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information,” en 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2019, págs. 311-317. [11] J. Lyu, S. Wang, T. E. Balius y col., “Ultra-large library docking for discovering new chemotypes,” Nature, vol. 566, n.o 7743, págs. 224-229, 2019. [12] D. Weininger, “SMILES, a chemical language and information system. 1. Intro- duction to methodology and encoding rules,” Journal of chemical information and computer sciences, vol. 28, n.o 1, págs. 31-36, 1988. [13] M. Krenn, F. Häse, A. Nigam, P. Friederich y A. Aspuru-Guzik, “Self-Referencing Embedded Strings (SELFIES): A 100 % robust molecular string representation,” Machine Learning: Science and Technology, vol. 1, n.o 4, pág. 045 024, 2020. [14] A. Vaswani, N. Shazeer, N. Parmar y col., “Attention is all you need,” en Ad- vances in neural information processing systems, 2017, págs. 5998-6008. [15] K. Rajan, A. Zielesny y C. Steinbeck, “DECIMER 1.0: Deep Learning for Che- mical Image Recognition using Transformers,” 2021. [16] D. Grechishnikova, “Transformer neural network for protein-specific de novo drug generation as a machine translation problem,” Scientific reports, vol. 11, n.o 1, págs. 1-13, 2021. [17] B. Shin, S. Park, K. Kang y J. C. Ho, “Self-attention based molecule representa- tion for predicting drug-target interaction,” en Machine Learning for Healthcare Conference, PMLR, 2019, págs. 230-248. [18] A. C.-L. Lee, J. L. Harris, K. K. Khanna y J.-H. Hong, “A comprehensive review on current advances in peptide drug development and design,” International journal of molecular sciences, vol. 20, n.o 10, pág. 2383, 2019. [19] R. B. Silverman y M. W. Holladay, The organic chemistry of drug design and drug action. Academic press, 2014. [20] R. A. Copeland, Enzymes: a practical introduction to structure, mechanism, and data analysis. John Wiley & Sons, 2000. [21] B. Pathare, V. Tambe y V. Patil, “A review on various analytical methods used in determination of dissociation constant,” Int. J. Pharm. Pharm. Sci, vol. 6, n.o 8, págs. 26-34, 2014. [22] H. Wiley, “LOIS GÉNÉRALES DE L’ACTION DES DIASTASES.,” Journal of the American Chemical Society, vol. 25, n.o 7, págs. 780-782, 1903. [23] L. Michaelis, M. L. Menten y col., “Die kinetik der invertinwirkung,” Biochem. z, vol. 49, n.o 333-369, pág. 352, 1913. [24] A. Cornish-Bowden, “One hundred years of Michaelis–Menten kinetics,” Pers- pectives in Science, vol. 4, págs. 3-9, 2015. [25] G. E. Briggs y J. B. S. Haldane, “A note on the kinetics of enzyme action,” Biochemical journal, vol. 19, n.o 2, pág. 338, 1925. [26] R. W. Fuller y L. R. Steranka, “Drug Discovery at the Enzyme Level,” en Drug Discovery and Development, Springer, 1987, págs. 177-198. [27] M. Aitken, E. R. Berndt y D. M. Cutler, “Prescription Drug Spending Trends In The United States: Looking Beyond The Turning Point: The drug spending trends observed in the 1980s, 1990s, and the first few years of this decade have changed dramatically in the past five years—bringing both opportunity and threat.,” Health Affairs, vol. 27, n.o Suppl1, w151-w160, 2008. [28] R. Chang y V. J. Lotti, “Biochemical and pharmacological characterization of an extremely potent and selective nonpeptide cholecystokinin antagonist,” Pro- ceedings of the National Academy of Sciences, vol. 83, n.o 13, págs. 4923-4926, 1986. [29] G. D. Stewart, C. Valant, S. J. Dowell y col., “Determination of adenosine A1 receptor agonist and antagonist pharmacology using Saccharomyces cerevisiae: implications for ligand screening and functional selectivity,” Journal of Pharma- cology and Experimental Therapeutics, vol. 331, n.o 1, págs. 277-286, 2009. [30] I. Carroll, J. B. Thomas, L. A. Dykstra y col., “Pharmacological properties of JDTic: A novel κ-opioid receptor antagonist,” European journal of pharmacology, vol. 501, n.o 1-3, págs. 111-119, 2004. [31] H. Bisswanger, Enzyme kinetics: principles and methods. John Wiley & Sons, 2017. [32] R. R. Neubig, M. Spedding, T. Kenakin y A. Christopoulos, “International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on terms and symbols in quantitative pharmacology,” Phar- macological reviews, vol. 55, n.o 4, págs. 597-606, 2003. [33] G. W Caldwell, Z. Yan, W. Lang y J. A Masucci, “The IC50 concept revisited,” Current topics in medicinal chemistry, vol. 12, n.o 11, págs. 1282-1290, 2012. [34] Z. Yan, B. Rafferty, G. Caldwell y J. Masucci, “Rapidly distinguishing rever- sible and irreversible CYP450 inhibitors by using fluorometric kinetic analy- ses,” European journal of drug metabolism and pharmacokinetics, vol. 27, n.o 4, págs. 281-287, 2002. [35] G. W. Caldwell, “ADME optimization and toxicity assessment in drug disco- very,” Frontiers in Medicinal Chemistry, vol. 8, pág. 3, 2016. [36] C. Yung-Chi y W. H. Prusoff, “Relationship between the inhibition constant (KI) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction,” Biochemical pharmacology, vol. 22, n.o 23, págs. 3099-3108, 1973. [37] R. Z. Cer, U. Mudunuri, R. Stephens y F. J. Lebeda, “IC 50-to-K i: a web-based tool for converting IC 50 to K i values for inhibitors of enzyme activity and ligand binding,” Nucleic acids research, vol. 37, n.o suppl_2, W441-W445, 2009. [38] M. Tom, “Mitchell: Machine Learning,” 1997 Burr Ridge, vol. 45, n.o 37, págs. 870-877, 1997. [39] S. Dey, S. Saha, A. K. Singh y K. McDonald-Maier, “SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities,” Smart Cities, vol. 5, n.o 1, págs. 162-176, 2022. [40] U. Sharma, A. Gupta y S. K. Gupta, “The pertinence of incorporating ESG ra- tings to make investment decisions: a quantitative analysis using machine lear- ning,” Journal of Sustainable Finance & Investment, págs. 1-15, 2022. [41] S. Singh, A. Anand, S. Mukherjee y T. Choudhury, “Machine Learning Ap- plications in Decision Intelligence Analytics,” en Decision Intelligence Analy- tics and the Implementation of Strategic Business Management, Springer, 2022, págs. 163-178. [42] S. Hayman, “The mcculloch-pitts model,” en IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), IEEE, vol. 6, 1999, págs. 4438-4439. [43] D. O. Hebb, “The first stage of perception: growth of the assembly,” The Orga- nization of Behavior, vol. 4, págs. 60-78, 1949. [44] P. P. Shinde y S. Shah, “A review of machine learning and deep learning appli- cations,” en 2018 Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, 2018, págs. 1-6. [45] C. Murphy, P. Gray y G. Stewart, “Verified perceptron convergence theorem,” en Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2017, págs. 43-50. [46] J. Schmidhuber, “Who invented backpropagation,” More in [DL2], 2014. [47] C. Cortes y V. Vapnik, “Support vector machine,” Machine learning, vol. 20, n.o 3, págs. 273-297, 1995. [48] J. E. Van Engelen y H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning, vol. 109, n.o 2, págs. 373-440, 2020. [49] Y. Baştanlar y M. Özuysal, “Introduction to machine learning,” miRNomics: MicroRNA biology and computational analysis, págs. 105-128, 2014. [50] Z. Ghahramani, “Unsupervised learning,” en Summer school on machine lear- ning, Springer, 2003, págs. 72-112. [51] S. Dridi, “Reinforcement Learning-A Systematic Literature Review,” 2022. [52] F. Hensel, M. Moor y B. Rieck, “A survey of topological machine learning methods,” Frontiers in Artificial Intelligence, vol. 4, pág. 52, 2021. [53] S. Guo, M. Agarwal, C. Cooper y col., “Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm,” Journal of Manufacturing Systems, vol. 62, págs. 145-163, 2022. [54] D. Morgan, G. Pilania, A. Couet, B. P. Uberuaga, C. Sun y J. Li, “Machine learning in nuclear materials research,” Current Opinion in Solid State and Ma- terials Science, vol. 26, n.o 2, pág. 100 975, 2022. [55] J. G. Greener, S. M. Kandathil, L. Moffat y D. T. Jones, “A guide to machine learning for biologists,” Nature Reviews Molecular Cell Biology, vol. 23, n.o 1, págs. 40-55, 2022. [56] K. G. Liakos, P. Busato, D. Moshou, S. Pearson y D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, n.o 8, pág. 2674, 2018. [57] D. Rolnick, P. L. Donti, L. H. Kaack y col., “Tackling climate change with machine learning,” ACM Computing Surveys (CSUR), vol. 55, n.o 2, págs. 1-96, 2022. [58] M. M. Ahsan y Z. Siddique, “Machine learning-based heart disease diagnosis: A systematic literature review,” Artificial Intelligence in Medicine, pág. 102 289, 2022. [59] Z. A. A. Alyasseri, M. A. Al-Betar, I. A. Doush y col., “Review on COVID- 19 diagnosis models based on machine learning and deep learning approaches,” Expert systems, vol. 39, n.o 3, e12759, 2022. [60] J. Vamathevan, D. Clark, P. Czodrowski y col., “Applications of machine learning in drug discovery and development,” Nature reviews Drug discovery, vol. 18, n.o 6, págs. 463-477, 2019. [61] M. Bagherian, E. Sabeti, K. Wang, M. A. Sartor, Z. Nikolovska-Coleska y K. Najarian, “Machine learning approaches and databases for prediction of drug– target interaction: a survey paper,” Briefings in bioinformatics, vol. 22, n.o 1, págs. 247-269, 2021. [62] R. Chen, X. Liu, S. Jin, J. Lin y J. Liu, “Machine learning for drug-target interaction prediction,” Molecules, vol. 23, n.o 9, pág. 2208, 2018. [63] M. Pak y S. Kim, “A review of deep learning in image recognition,” en 2017 4th international conference on computer applications and information processing technology (CAIPT), IEEE, 2017, págs. 1-3. [64] H. Leung y S. Haykin, “The complex backpropagation algorithm,” IEEE Transac- tions on signal processing, vol. 39, n.o 9, págs. 2101-2104, 1991. [65] M. M. Mijwel, “Artificial neural networks advantages and disadvantages,” Retrie- ved from LinkedIn https//www. linkedin. com/pulse/artificial-neuralnet Work, 2018. [66] E. Byvatov, U. Fechner, J. Sadowski y G. Schneider, “Comparison of support vector machine and artificial neural network systems for drug/nondrug classifi- cation,” Journal of chemical information and computer sciences, vol. 43, n.o 6, págs. 1882-1889, 2003. [67] A. Kurani, P. Doshi, A. Vakharia y M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Annals of Data Science, págs. 1-26, 2021. [68] H. Bennett-Lenane, B. T. Griffin y J. P. O’Shea, “Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks,” European Journal of Pharmaceutical Sciences, vol. 168, pág. 106 018, 2022. [69] T. Pearce, F. Leibfried y A. Brintrup, “Uncertainty in neural networks: Appro- ximately bayesian ensembling,” en International conference on artificial intelli- gence and statistics, PMLR, 2020, págs. 234-244. [70] D. Bahdanau, K. Cho e Y. Bengio, “Neural machine translation by jointly lear- ning to align and translate,” arXiv preprint arXiv:1409.0473, 2014. [71] K. Xu, J. Ba, R. Kiros y col., “Show, attend and tell: Neural image caption gene- ration with visual attention,” en International conference on machine learning, PMLR, 2015, págs. 2048-2057. [72] Z. Yang, D. Yang, C. Dyer, X. He, A. Smola y E. Hovy, “Hierarchical attention networks for document classification,” en Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, 2016, págs. 1480-1489. [73] K. S. Kalyan, A. Rajasekharan y S. Sangeetha, “Ammus: A survey of transformer- based pretrained models in natural language processing,” arXiv preprint ar- Xiv:2108.05542, 2021. [74] S. Edunov, A. Baevski y M. Auli, “Pre-trained language model representations for language generation,” arXiv preprint arXiv:1903.09722, 2019. [75] R. Dale, “GPT-3: What’s it good for?” Natural Language Engineering, vol. 27, n.o 1, págs. 113-118, 2021. [76] J. Devlin, M.-W. Chang, K. Lee y K. Toutanova, “Bert: Pre-training of deep bidi- rectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [77] Y. Liu, M. Ott, N. Goyal y col., “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019. [78] R. Scheible, F. Thomczyk, P. Tippmann, V. Jaravine y M. Boeker, “Gottbert: a pure german language model,” arXiv preprint arXiv:2012.02110, 2020. [79] P. Delobelle, T. Winters y B. Berendt, “Robbert: a dutch roberta-based language model,” arXiv preprint arXiv:2001.06286, 2020. [80] E. Providel y M. Mendoza, “Misleading information in Spanish: a survey,” Social Network Analysis and Mining, vol. 11, n.o 1, págs. 1-26, 2021. [81] S. M. Paul, D. S. Mytelka, C. T. Dunwiddie y col., “How to improve R&D productivity: the pharmaceutical industry’s grand challenge,” Nature reviews Drug discovery, vol. 9, n.o 3, págs. 203-214, 2010. [82] M. Dickson y J. P. Gagnon, “Key factors in the rising cost of new drug discovery and development,” Nature reviews Drug discovery, vol. 3, n.o 5, págs. 417-429, 2004. [83] H. Xue, J. Li, H. Xie e Y. Wang, “Review of drug repositioning approaches and resources,” International journal of biological sciences, vol. 14, n.o 10, pág. 1232, 2018. [84] A. F. Moumbock, J. Li, P. Mishra, M. Gao y S. Günther, “Current computational methods for predicting protein interactions of natural products,” Computational and Structural Biotechnology Journal, vol. 17, págs. 1367-1376, 2019. [85] J. Fan, A. Fu y L. Zhang, “Progress in molecular docking,” Quantitative Biology, vol. 7, n.o 2, págs. 83-89, 2019. [86] A. Ezzat, M. Wu, X.-L. Li y C.-K. Kwoh, “Computational prediction of drug– target interactions using chemogenomic approaches: an empirical survey,” Brie- fings in bioinformatics, vol. 20, n.o 4, págs. 1337-1357, 2019. [87] T. Pahikkala, A. Airola, S. Pietilä y col., “Toward more realistic drug–target interaction predictions,” Briefings in bioinformatics, vol. 16, n.o 2, págs. 325-337, 2015. [88] T. Fawcett, “An introduction to ROC analysis,” Pattern recognition letters, vol. 27, n.o 8, págs. 861-874, 2006. [89] J. Davis y M. Goadrich, “The relationship between Precision-Recall and ROC curves,” en Proceedings of the 23rd international conference on Machine learning, 2006, págs. 233-240. [90] K. Abbasi, P. Razzaghi, A. Poso, S. Ghanbari-Ara y A. Masoudi-Nejad, “Deep learning in drug target interaction prediction: current and future perspectives,” Current Medicinal Chemistry, vol. 28, n.o 11, págs. 2100-2113, 2021. [91] M. Wen, Z. Zhang, S. Niu y col., “Deep-learning-based drug–target interaction prediction,” Journal of proteome research, vol. 16, n.o 4, págs. 1401-1409, 2017. [92] H. Öztürk, A. Özgür y E. Ozkirimli, “DeepDTA: deep drug–target binding affi- nity prediction,” Bioinformatics, vol. 34, n.o 17, págs. i821-i829, 2018. [93] M. Hassan, D. C. Mogollon, O. Fuentes y col., “DLSCORE: A deep learning model for predicting protein-ligand binding affinities,” 2018. [94] L. Xie, S. He, X. Song, X. Bo y Z. Zhang, “Deep learning-based transcripto- me data classification for drug-target interaction prediction,” BMC genomics, vol. 19, n.o 7, págs. 93-102, 2018. [95] Q. Feng, E. Dueva, A. Cherkasov y M. Ester, “Padme: A deep learning-based fra- mework for drug-target interaction prediction,” arXiv preprint arXiv:1807.09741, 2018. [96] M. Tsubaki, K. Tomii y J. Sese, “Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences,” Bioinforma- tics, vol. 35, n.o 2, págs. 309-318, 2019. [97] M. Karimi, D. Wu, Z. Wang e Y. Shen, “DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks,” Bioinformatics, vol. 35, n.o 18, págs. 3329-3338, 2019. [98] I. Lee, J. Keum y H. Nam, “DeepConv-DTI: Prediction of drug-target interac- tions via deep learning with convolution on protein sequences,” PLoS compu- tational biology, vol. 15, n.o 6, e1007129, 2019. [99] B. Shin, S. Park, K. Kang y J. C. Ho, “Self-attention based molecule representa- tion for predicting drug-target interaction,” en Machine Learning for Healthcare Conference, PMLR, 2019, págs. 230-248. [100] K. Huang, C. Xiao, L. M. Glass y J. Sun, “MolTrans: Molecular Interaction Transformer for drug–target interaction prediction,” Bioinformatics, vol. 37, n.o 6, págs. 830-836, 2021. [101] K. Abbasi, P. Razzaghi, A. Poso, M. Amanlou, J. B. Ghasemi y A. Masoudi- Nejad, “DeepCDA: deep cross-domain compound–protein affinity prediction th- rough LSTM and convolutional neural networks,” Bioinformatics, vol. 36, n.o 17, págs. 4633-4642, 2020. [102] Z. Yang, W. Zhong, L. Zhao y C. Y.-C. Chen, “MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction,” Chemical Science, 2022. [103] I. Lee y H. Nam, “Sequence-based prediction of protein binding regions and drug–target interactions,” Journal of cheminformatics, vol. 14, n.o 1, págs. 1-15, 2022. [104] P. Zhang, Z. Wei, C. Che y B. Jin, “DeepMGT-DTI: Transformer network incor- porating multilayer graph information for Drug–Target interaction prediction,” Computers in biology and medicine, pág. 105 214, 2022. [105] F. Cui, Z. Zhang y Q. Zou, “Sequence representation approaches for sequence- based protein prediction tasks that use deep learning,” Briefings in functional genomics, vol. 20, n.o 1, págs. 61-73, 2021. [106] X. Chen, B. Ren, M. Chen y col., “ASDCD: antifungal synergistic drug combi- nation database,” PloS one, vol. 9, n.o 1, e86499, 2014. [107] T. Liu, Y. Lin, X. Wen, R. N. Jorissen y M. K. Gilson, “BindingDB: a web- accessible database of experimentally determined protein–ligand binding affini- ties,” Nucleic acids research, vol. 35, n.o suppl_1, págs. D198-D201, 2007. [108] S. Placzek, I. Schomburg, A. Chang y col., “BRENDA in 2017: new perspectives and new tools in BRENDA,” Nucleic acids research, gkw952, 2016. [109] R. Kumar, K. Chaudhary, S. Gupta y col., “CancerDR: cancer drug resistance database,” Scientific reports, vol. 3, n.o 1, págs. 1-6, 2013. [110] K. P. Seiler, G. A. George, M. P. Happ y col., “ChemBank: a small-molecule screening and cheminformatics resource database,” Nucleic acids research, vol. 36, n.o suppl_1, págs. D351-D359, 2007. [111] D. Mendez, A. Gaulton, A. P. Bento y col., “ChEMBL: towards direct deposition of bioassay data,” Nucleic acids research, vol. 47, n.o D1, págs. D930-D940, 2019. [112] J. Kringelum, S. K. Kjaerulff, S. Brunak, O. Lund, T. I. Oprea y O. Taboureau, “ChemProt-3.0: a global chemical biology diseases mapping,” Database, vol. 2016, 2016. [113] Y. Liu, Q. Wei, G. Yu, W. Gai, Y. Li y X. Chen, “DCDB 2.0: a major update of the drug combination database,” Database, vol. 2014, 2014. [114] K. C. Cotto, A. H. Wagner, Y.-Y. Feng y col., “DGIdb 3.0: a redesign and expansion of the drug–gene interaction database,” Nucleic acids research, vol. 46, n.o D1, págs. D1068-D1073, 2018. [115] D. S. Wishart, Y. D. Feunang, A. C. Guo y col., “DrugBank 5.0: a major update to the DrugBank database for 2018,” Nucleic acids research, vol. 46, n.o D1, págs. D1074-D1082, 2018. [116] A. J. Pawson, J. L. Sharman, H. E. Benson y col., “The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands,” Nucleic acids research, vol. 42, n.o D1, págs. D1098-D1106, 2014. [117] S. Orchard, M. Ammari, B. Aranda y col., “The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases,” Nucleic acids research, vol. 42, n.o D1, págs. D358-D363, 2014. [118] M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato y K. Morishima, “KEGG: new perspectives on genomes, pathways, diseases and drugs,” Nucleic acids research, vol. 45, n.o D1, págs. D353-D361, 2017. [119] S. Günther, M. Kuhn, M. Dunkel y col., “SuperTarget and Matador: resources for exploring drug-target relationships,” Nucleic acids research, vol. 36, n.o suppl_1, págs. D919-D922, 2007. [120] J. Von Eichborn, M. S. Murgueitio, M. Dunkel, S. Koerner, P. E. Bourne y R. Preissner, “PROMISCUOUS: a database for network-based drug-repositioning,” Nucleic acids research, vol. 39, n.o suppl_1, págs. D1060-D1066, 2010. [121] S. Kim, P. A. Thiessen, E. E. Bolton y col., “PubChem substance and compound databases,” Nucleic acids research, vol. 44, n.o D1, págs. D1202-D1213, 2016. [122] M. Kuhn, M. Campillos, I. Letunic, L. J. Jensen y P. Bork, “A side effect resource to capture phenotypic effects of drugs,” Molecular systems biology, vol. 6, n.o 1, pág. 343, 2010. [123] D. Szklarczyk, A. Santos, C. Von Mering, L. J. Jensen, P. Bork y M. Kuhn, “STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data,” Nucleic acids research, vol. 44, n.o D1, págs. D380-D384, 2016. [124] J. Nickel, B.-O. Gohlke, J. Erehman y col., “SuperPred: update on drug classifi- cation and target prediction,” Nucleic acids research, vol. 42, n.o W1, W26-W31, 2014. [125] N. Hecker, J. Ahmed, J. von Eichborn y col., “SuperTarget goes quantitative: update on drug–target interactions,” Nucleic acids research, vol. 40, n.o D1, págs. D1113-D1117, 2012. [126] M. P. Magariños, S. J. Carmona, G. J. Crowther y col., “TDR Targets: a chemo- genomics resource for neglected diseases,” Nucleic acids research, vol. 40, n.o D1, págs. D1118-D1127, 2012. [127] C. Qin, C. Zhang, F. Zhu y col., “Therapeutic target database update 2014: a resource for targeted therapeutics,” Nucleic acids research, vol. 42, n.o D1, págs. D1118-D1123, 2014. [128] K. Roy y S. Kar, “The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on ‘Is regression through origin useful in external validation of QSAR models?’)” European Journal of Pharmaceutical Sciences, vol. 62, págs. 111-114, 2014. [129] A. R. Brentnall y J. Cuzick, “Use of the concordance index for predictors of censored survival data,” Statistical methods in medical research, vol. 27, n.o 8, págs. 2359-2373, 2018. [130] K. Huang, T. Fu, W. Gao y col., “Therapeutics Data Commons: Machine lear- ning datasets and tasks for drug discovery and development,” arXiv preprint arXiv:2102.09548, 2021. [131] J. Tang, A. Szwajda, S. Shakyawar y col., “Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis,” Journal of Chemical Information and Modeling, vol. 54, n.o 3, págs. 735-743, 2014. [132] R. Sennrich, B. Haddow y A. Birch, “Neural machine translation of rare words with subword units,” arXiv preprint arXiv:1508.07909, 2015. [133] S. Chithrananda, G. Grand y B. Ramsundar, “Chemberta: Large-scale self- supervised pretraining for molecular property prediction,” arXiv preprint ar- Xiv:2010.09885, 2020. [134] A. Nambiar, M. Heflin, S. Liu, S. Maslov, M. Hopkins y A. Ritz, “Transforming the language of life: Transformer neural networks for protein prediction tasks,” en Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2020, págs. 1-8. [135] M. Filipavicius, M. Manica, J. Cadow y M. R. Martinez, “Pre-training pro- tein language models with label-agnostic binding pairs enhances performance in downstream tasks,” arXiv preprint arXiv:2012.03084, 2020. [136] A. Paszke, S. Gross, F. Massa y col., “Pytorch: An imperative style, high- performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019.
dc.rights.license.none.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rights.uri.none.fl_str_mv	https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.coar.none.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.accessrights.none.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.none.fl_str_mv	169 páginas
dc.format.mimetype.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Universidad Tecnológica de Pereira
dc.publisher.place.none.fl_str_mv	Pereira
publisher.none.fl_str_mv	Universidad Tecnológica de Pereira
institution	Universidad Tecnológica de Pereira
bitstream.url.fl_str_mv	https://repositorio.utp.edu.co/bitstreams/5f39c41c-516c-4e2c-8eff-7d1ab2667010/download https://repositorio.utp.edu.co/bitstreams/1af2f147-9925-4389-8bba-1a8445148eeb/download https://repositorio.utp.edu.co/bitstreams/c798bf44-677d-4362-afbe-d4d20d69cad1/download https://repositorio.utp.edu.co/bitstreams/db269042-356b-41b7-9ced-66a8557530b0/download https://repositorio.utp.edu.co/bitstreams/2ee66e9a-1750-4c49-815b-c712bfa6dd35/download
bitstream.checksum.fl_str_mv	282b6a53ccc972531fa22bf157277e74 73a5432e0b76442b22b026844140d683 90447339de12d2679cc6e0cc864b85ec a10543daca66a5b0006cc7939996fb56 39f94df051fd3a03eebe6a659c028a50
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio de la Universidad Tecnológica de Pereira
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
_version_	1828202101147172864
spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 de la Ley 23 de 1982. En concordancia suscribo (suscribimos) este documento en el momento mismo que hago (hacemos) entrega de mi (nuestra) OBRA a la Biblioteca “Jorge Roa Martínez” de la Universidad Tecnológica de Pereira. Manifiesto (manifestamos) que la OBRA objeto de la presente autorizaciónhttps://creativecommons.org/licenses/by-nc-sa/4.0/http://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessJaramillo Villegas, José AlfredoRosero Arias, Cristian2024-11-27T16:55:21Z2024-11-27T16:55:21Z2024https://hdl.handle.net/11059/15513ISBN: 978-958-722-954-7https://doi.org/10.22517/9789587229547.Universidad Tecnológica de PereiraRepositorio Universidad Tecnológica de Pereirahttps://repositorio.utp.edu.co/home: figuras, tablasReconocer la interacción fármaco-objetivo (DTI) es una tarea crucial en el descubrimiento de fármacos in silico, que es costoso y requiere mucho tiempo debido al gran alcance de la búsqueda de posibles fármacos en un amplio espacio de compuestos farmacológicos. Los últimos años han sido testigos de estrategias prometedoras de predicción de DTI basadas en el aprendizaje profundo. Adicionalmente, las tareas de aprendizaje de regresión pueden acercarse a la predicción de afinidad fármaco-objetivo (DTA) ofreciendo más interpretación biológica. Además, la arquitectura Transformer basada en modelos de atención y las técnicas de aprendizaje transferido han despertado interés en la predicción de la DTI. Sin embargo, hasta donde sabemos, hay pocos trabajos implementando Transformer pre-entrenados. Proponemos una arquitectura to- talmente basada en Transformers (RobertDTA/DTI) para explorar los efectos de los modelos de atención y transferencia de aprendizaje para la predicción del DTI/DTA. RobertDTA/DTI se evalúa con datos experimentales presentando un rendimiento alto en comparación con modelos de base de última generación, sin perder interpretabilidad molecular en sus resultados. Este trabajo proporciona una nueva línea de base de modelos moleculares basados en Transformer y arquitecturas pre-entrenadas para la predicción de DTI.CAPÍTULO 1 Introducción - 21 1.1 Descripción del problema - 23 1.2 Formulación del problema - 25 1.3 Objetivos de investigación - 27 1.3.1 Objetivo general - 27 1.3.2 Objetivos específicos - 27 1.4 Antecedentes y justificación - 28 1.5 Síntesis metodológica - 29 1.5.1 Hipótesis - 29 1.5.2 Resumen metodológico - 30 CAPÍTULO 2 Interacción Fármaco Objetivo - 31 2.1 Actividad farmacológica - 33 2.1.1 Introducción - 33 2.1.2 Constante de disociación en el equilibrio, Kd - 34 2.1.3 Catálisis enzimática - 36 2.1.4 Fármacos o principio activo - 41 2.2 Inhibidores reversibles - 45 2.2.1 Modos de inhibición - 49 2.2.2 Relación entre los valores de afinidad: IC50 y Ki - 50 Índice general CAPÍTULO 3 Fundamentos en aprendizaje de máquina - 55 3.1 Introducción al aprendizaje de máquina - 57 3.1.1 Breve historia del aprendizaje de máquina - 58 3.1.2 Tipos de aprendizaje - 60 3.1.3 Algoritmos convencionales de aprendizaje de máquina - 62 3.1.4 Aplicaciones del aprendizaje de máquina - 69 3.2 Aprendizaje profundo - 70 3.2.1 Arquitecturas convencionales de aprendizaje profundo -72 3.2.2 Arquitectura Transformer y mecanismo de atención - 76 3.2.3 Aprendizaje transferido -79 3.3 Métricas - 81 3.3.1 Funciones de pérdida - 82 3.3.2 Métricas en regresión - 85 3.3.3 Métricas en clasificación binaria - 86 CAPÍTULO 4 Interacción Fármaco Objetivo In Silico - 93 4.1 Introducción - 95 4.2 Métodos computacionales para fármaco objetivo - 96 4.3 Arquitecturas de aprendizaje profundo para DTI - 99 4.4 Bases de datos para DTI -104 4.5 Resultados reportados en el estado del arte para DTI/DTA - 106 4.5.1 Tarea de clasificación: Predicción de DTI - 106 4.5.2 Tarea de regresión: predicción de DTA -108 CAPÍTULO 5 Desarrollo de la tesis: Diseño de la arquitectura - 111 5.1 Introducción - 113 5.2 Marcos de datos - 114 5.3 Metodología del diseño - 117 5.3.1 Módulos para extracción de características - 117 5.3.2 Estudio de ablación: Análisis de configuraciones - 121 5.4 Implementación - 125 CAPÍTULO 6 Resultados y discusión - 127 6.1 Rober-DTI: Tarea de clasificación - 129 6.1.1 Remoción del módulo de interacción - 130 6.2 SMILES vs SELFIES - 133 6.3 Rober-DTA: Tarea de regresión -138 6.4 Interpretación biológica de los datos - 140 CAPÍTULO 7 Conclusión - 143 7.1 Conclusiones: Limitaciones y recomendaciones - 145 CAPÍTULO A Despliegue de interfaz gráfica -149 A.1 Tecnologías utilizadas en la implementación de la interfaz gráfica - 151 A.1.1 Repositorio y despliegue de la interfaz - 153 Bibliografía - 155169 páginasapplication/pdfspaUniversidad Tecnológica de PereiraPereira370 - EducaciónInteligencia artificialAprendizaje automático (Inteligencia artificial)BiotecnologíaInteligencia artificialAprendizaje automáticoBioinformáticaBiología computacionalBiotecnologíaTeoría de grafosFarmacologíaRoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivoLibroinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_2f33Textinfo:eu-repo/semantics/book[1] J.-F. Wang, D.-Q. Wei y K.-C. Chou, “Pharmacogenomics and personalized use of drugs,” Current topics in medicinal chemistry, vol. 8, n.o 18, págs. 1573-1579, 2008.[2] Instituto Nacional de Salud, Observatorio Nacional de Salud, “COVID-19: pro- greso de la pandemia y sus desigualdades en Colombia; Décimo tercero Informe Técnico,” Instituto Nacional de Salud, inf. téc., 2021, ISSN: 2346-3325.[3] T. He, M. Heidemeyer, F. Ban, A. Cherkasov y M. Ester, “SimBoost: a read- across approach for predicting drug–target binding affinities using gradient boos- ting machines,” Journal of cheminformatics, vol. 9, n.o 1, págs. 1-14, 2017.[4] J. A. DiMasi, H. G. Grabowski y R. W. Hansen, “Innovation in the pharma- ceutical industry: new estimates of R&D costs,” Journal of health economics, vol. 47, págs. 20-33, 2016.[5] Z. Wu, B. Ramsundar, E. N. Feinberg y col., “MoleculeNet: a benchmark for molecular machine learning,” Chemical science, vol. 9, n.o 2, págs. 513-530, 2018.[6] J. Li, A. Fu y L. Zhang, “An overview of scoring functions used for protein–ligand interactions in molecular docking,” Interdisciplinary Sciences: Computational Life Sciences, vol. 11, n.o 2, págs. 320-328, 2019.[7] D. R. Koes, M. P. Baumgartner y C. J. Camacho, “Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise,” Journal of chemical information and modeling, vol. 53, n.o 8, págs. 1893-1904, 2013.[8] N. M. Hassan, A. A. Alhossary, Y. Mu y C.-K. Kwoh, “Protein-ligand blind doc- king using QuickVina-W with inter-process spatio-temporal integration,” Scien- tific reports, vol. 7, n.o 1, págs. 1-13, 2017.[9] J. C. Pereira, E. R. Caffarena y C. N. Dos Santos, “Boosting docking-based virtual screening with deep learning,” Journal of chemical information and mo- deling, vol. 56, n.o 12, págs. 2495-2506, 2016.[10] Z. Liao, R. You, X. Huang, X. Yao, T. Huang y S. Zhu, “DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information,” en 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2019, págs. 311-317.[11] J. Lyu, S. Wang, T. E. Balius y col., “Ultra-large library docking for discovering new chemotypes,” Nature, vol. 566, n.o 7743, págs. 224-229, 2019.[12] D. Weininger, “SMILES, a chemical language and information system. 1. Intro- duction to methodology and encoding rules,” Journal of chemical information and computer sciences, vol. 28, n.o 1, págs. 31-36, 1988.[13] M. Krenn, F. Häse, A. Nigam, P. Friederich y A. Aspuru-Guzik, “Self-Referencing Embedded Strings (SELFIES): A 100 % robust molecular string representation,” Machine Learning: Science and Technology, vol. 1, n.o 4, pág. 045 024, 2020.[14] A. Vaswani, N. Shazeer, N. Parmar y col., “Attention is all you need,” en Ad- vances in neural information processing systems, 2017, págs. 5998-6008.[15] K. Rajan, A. Zielesny y C. Steinbeck, “DECIMER 1.0: Deep Learning for Che- mical Image Recognition using Transformers,” 2021.[16] D. Grechishnikova, “Transformer neural network for protein-specific de novo drug generation as a machine translation problem,” Scientific reports, vol. 11, n.o 1, págs. 1-13, 2021.[17] B. Shin, S. Park, K. Kang y J. C. Ho, “Self-attention based molecule representa- tion for predicting drug-target interaction,” en Machine Learning for Healthcare Conference, PMLR, 2019, págs. 230-248.[18] A. C.-L. Lee, J. L. Harris, K. K. Khanna y J.-H. Hong, “A comprehensive review on current advances in peptide drug development and design,” International journal of molecular sciences, vol. 20, n.o 10, pág. 2383, 2019.[19] R. B. Silverman y M. W. Holladay, The organic chemistry of drug design and drug action. Academic press, 2014.[20] R. A. Copeland, Enzymes: a practical introduction to structure, mechanism, and data analysis. John Wiley & Sons, 2000.[21] B. Pathare, V. Tambe y V. Patil, “A review on various analytical methods used in determination of dissociation constant,” Int. J. Pharm. Pharm. Sci, vol. 6, n.o 8, págs. 26-34, 2014.[22] H. Wiley, “LOIS GÉNÉRALES DE L’ACTION DES DIASTASES.,” Journal of the American Chemical Society, vol. 25, n.o 7, págs. 780-782, 1903.[23] L. Michaelis, M. L. Menten y col., “Die kinetik der invertinwirkung,” Biochem. z, vol. 49, n.o 333-369, pág. 352, 1913.[24] A. Cornish-Bowden, “One hundred years of Michaelis–Menten kinetics,” Pers- pectives in Science, vol. 4, págs. 3-9, 2015.[25] G. E. Briggs y J. B. S. Haldane, “A note on the kinetics of enzyme action,” Biochemical journal, vol. 19, n.o 2, pág. 338, 1925.[26] R. W. Fuller y L. R. Steranka, “Drug Discovery at the Enzyme Level,” en Drug Discovery and Development, Springer, 1987, págs. 177-198.[27] M. Aitken, E. R. Berndt y D. M. Cutler, “Prescription Drug Spending Trends In The United States: Looking Beyond The Turning Point: The drug spending trends observed in the 1980s, 1990s, and the first few years of this decade have changed dramatically in the past five years—bringing both opportunity and threat.,” Health Affairs, vol. 27, n.o Suppl1, w151-w160, 2008.[28] R. Chang y V. J. Lotti, “Biochemical and pharmacological characterization of an extremely potent and selective nonpeptide cholecystokinin antagonist,” Pro- ceedings of the National Academy of Sciences, vol. 83, n.o 13, págs. 4923-4926, 1986.[29] G. D. Stewart, C. Valant, S. J. Dowell y col., “Determination of adenosine A1 receptor agonist and antagonist pharmacology using Saccharomyces cerevisiae: implications for ligand screening and functional selectivity,” Journal of Pharma- cology and Experimental Therapeutics, vol. 331, n.o 1, págs. 277-286, 2009.[30] I. Carroll, J. B. Thomas, L. A. Dykstra y col., “Pharmacological properties of JDTic: A novel κ-opioid receptor antagonist,” European journal of pharmacology, vol. 501, n.o 1-3, págs. 111-119, 2004.[31] H. Bisswanger, Enzyme kinetics: principles and methods. John Wiley & Sons, 2017.[32] R. R. Neubig, M. Spedding, T. Kenakin y A. Christopoulos, “International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on terms and symbols in quantitative pharmacology,” Phar- macological reviews, vol. 55, n.o 4, págs. 597-606, 2003.[33] G. W Caldwell, Z. Yan, W. Lang y J. A Masucci, “The IC50 concept revisited,” Current topics in medicinal chemistry, vol. 12, n.o 11, págs. 1282-1290, 2012.[34] Z. Yan, B. Rafferty, G. Caldwell y J. Masucci, “Rapidly distinguishing rever- sible and irreversible CYP450 inhibitors by using fluorometric kinetic analy- ses,” European journal of drug metabolism and pharmacokinetics, vol. 27, n.o 4, págs. 281-287, 2002.[35] G. W. Caldwell, “ADME optimization and toxicity assessment in drug disco- very,” Frontiers in Medicinal Chemistry, vol. 8, pág. 3, 2016.[36] C. Yung-Chi y W. H. Prusoff, “Relationship between the inhibition constant (KI) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction,” Biochemical pharmacology, vol. 22, n.o 23, págs. 3099-3108, 1973.[37] R. Z. Cer, U. Mudunuri, R. Stephens y F. J. Lebeda, “IC 50-to-K i: a web-based tool for converting IC 50 to K i values for inhibitors of enzyme activity and ligand binding,” Nucleic acids research, vol. 37, n.o suppl_2, W441-W445, 2009.[38] M. Tom, “Mitchell: Machine Learning,” 1997 Burr Ridge, vol. 45, n.o 37, págs. 870-877, 1997.[39] S. Dey, S. Saha, A. K. Singh y K. McDonald-Maier, “SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities,” Smart Cities, vol. 5, n.o 1, págs. 162-176, 2022.[40] U. Sharma, A. Gupta y S. K. Gupta, “The pertinence of incorporating ESG ra- tings to make investment decisions: a quantitative analysis using machine lear- ning,” Journal of Sustainable Finance & Investment, págs. 1-15, 2022.[41] S. Singh, A. Anand, S. Mukherjee y T. Choudhury, “Machine Learning Ap- plications in Decision Intelligence Analytics,” en Decision Intelligence Analy- tics and the Implementation of Strategic Business Management, Springer, 2022, págs. 163-178.[42] S. Hayman, “The mcculloch-pitts model,” en IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), IEEE, vol. 6, 1999, págs. 4438-4439.[43] D. O. Hebb, “The first stage of perception: growth of the assembly,” The Orga- nization of Behavior, vol. 4, págs. 60-78, 1949.[44] P. P. Shinde y S. Shah, “A review of machine learning and deep learning appli- cations,” en 2018 Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, 2018, págs. 1-6.[45] C. Murphy, P. Gray y G. Stewart, “Verified perceptron convergence theorem,” en Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2017, págs. 43-50.[46] J. Schmidhuber, “Who invented backpropagation,” More in [DL2], 2014.[47] C. Cortes y V. Vapnik, “Support vector machine,” Machine learning, vol. 20, n.o 3, págs. 273-297, 1995.[48] J. E. Van Engelen y H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning, vol. 109, n.o 2, págs. 373-440, 2020.[49] Y. Baştanlar y M. Özuysal, “Introduction to machine learning,” miRNomics: MicroRNA biology and computational analysis, págs. 105-128, 2014.[50] Z. Ghahramani, “Unsupervised learning,” en Summer school on machine lear- ning, Springer, 2003, págs. 72-112.[51] S. Dridi, “Reinforcement Learning-A Systematic Literature Review,” 2022.[52] F. Hensel, M. Moor y B. Rieck, “A survey of topological machine learning methods,” Frontiers in Artificial Intelligence, vol. 4, pág. 52, 2021.[53] S. Guo, M. Agarwal, C. Cooper y col., “Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm,” Journal of Manufacturing Systems, vol. 62, págs. 145-163, 2022.[54] D. Morgan, G. Pilania, A. Couet, B. P. Uberuaga, C. Sun y J. Li, “Machine learning in nuclear materials research,” Current Opinion in Solid State and Ma- terials Science, vol. 26, n.o 2, pág. 100 975, 2022.[55] J. G. Greener, S. M. Kandathil, L. Moffat y D. T. Jones, “A guide to machine learning for biologists,” Nature Reviews Molecular Cell Biology, vol. 23, n.o 1, págs. 40-55, 2022.[56] K. G. Liakos, P. Busato, D. Moshou, S. Pearson y D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, n.o 8, pág. 2674, 2018.[57] D. Rolnick, P. L. Donti, L. H. Kaack y col., “Tackling climate change with machine learning,” ACM Computing Surveys (CSUR), vol. 55, n.o 2, págs. 1-96, 2022.[58] M. M. Ahsan y Z. Siddique, “Machine learning-based heart disease diagnosis: A systematic literature review,” Artificial Intelligence in Medicine, pág. 102 289, 2022.[59] Z. A. A. Alyasseri, M. A. Al-Betar, I. A. Doush y col., “Review on COVID- 19 diagnosis models based on machine learning and deep learning approaches,” Expert systems, vol. 39, n.o 3, e12759, 2022.[60] J. Vamathevan, D. Clark, P. Czodrowski y col., “Applications of machine learning in drug discovery and development,” Nature reviews Drug discovery, vol. 18, n.o 6, págs. 463-477, 2019.[61] M. Bagherian, E. Sabeti, K. Wang, M. A. Sartor, Z. Nikolovska-Coleska y K. Najarian, “Machine learning approaches and databases for prediction of drug– target interaction: a survey paper,” Briefings in bioinformatics, vol. 22, n.o 1, págs. 247-269, 2021.[62] R. Chen, X. Liu, S. Jin, J. Lin y J. Liu, “Machine learning for drug-target interaction prediction,” Molecules, vol. 23, n.o 9, pág. 2208, 2018.[63] M. Pak y S. Kim, “A review of deep learning in image recognition,” en 2017 4th international conference on computer applications and information processing technology (CAIPT), IEEE, 2017, págs. 1-3.[64] H. Leung y S. Haykin, “The complex backpropagation algorithm,” IEEE Transac- tions on signal processing, vol. 39, n.o 9, págs. 2101-2104, 1991.[65] M. M. Mijwel, “Artificial neural networks advantages and disadvantages,” Retrie- ved from LinkedIn https//www. linkedin. com/pulse/artificial-neuralnet Work, 2018.[66] E. Byvatov, U. Fechner, J. Sadowski y G. Schneider, “Comparison of support vector machine and artificial neural network systems for drug/nondrug classifi- cation,” Journal of chemical information and computer sciences, vol. 43, n.o 6, págs. 1882-1889, 2003.[67] A. Kurani, P. Doshi, A. Vakharia y M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Annals of Data Science, págs. 1-26, 2021.[68] H. Bennett-Lenane, B. T. Griffin y J. P. O’Shea, “Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks,” European Journal of Pharmaceutical Sciences, vol. 168, pág. 106 018, 2022.[69] T. Pearce, F. Leibfried y A. Brintrup, “Uncertainty in neural networks: Appro- ximately bayesian ensembling,” en International conference on artificial intelli- gence and statistics, PMLR, 2020, págs. 234-244.[70] D. Bahdanau, K. Cho e Y. Bengio, “Neural machine translation by jointly lear- ning to align and translate,” arXiv preprint arXiv:1409.0473, 2014.[71] K. Xu, J. Ba, R. Kiros y col., “Show, attend and tell: Neural image caption gene- ration with visual attention,” en International conference on machine learning, PMLR, 2015, págs. 2048-2057.[72] Z. Yang, D. Yang, C. Dyer, X. He, A. Smola y E. Hovy, “Hierarchical attention networks for document classification,” en Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, 2016, págs. 1480-1489.[73] K. S. Kalyan, A. Rajasekharan y S. Sangeetha, “Ammus: A survey of transformer- based pretrained models in natural language processing,” arXiv preprint ar- Xiv:2108.05542, 2021.[74] S. Edunov, A. Baevski y M. Auli, “Pre-trained language model representations for language generation,” arXiv preprint arXiv:1903.09722, 2019.[75] R. Dale, “GPT-3: What’s it good for?” Natural Language Engineering, vol. 27, n.o 1, págs. 113-118, 2021.[76] J. Devlin, M.-W. Chang, K. Lee y K. Toutanova, “Bert: Pre-training of deep bidi- rectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.[77] Y. Liu, M. Ott, N. Goyal y col., “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.[78] R. Scheible, F. Thomczyk, P. Tippmann, V. Jaravine y M. Boeker, “Gottbert: a pure german language model,” arXiv preprint arXiv:2012.02110, 2020.[79] P. Delobelle, T. Winters y B. Berendt, “Robbert: a dutch roberta-based language model,” arXiv preprint arXiv:2001.06286, 2020.[80] E. Providel y M. Mendoza, “Misleading information in Spanish: a survey,” Social Network Analysis and Mining, vol. 11, n.o 1, págs. 1-26, 2021.[81] S. M. Paul, D. S. Mytelka, C. T. Dunwiddie y col., “How to improve R&D productivity: the pharmaceutical industry’s grand challenge,” Nature reviews Drug discovery, vol. 9, n.o 3, págs. 203-214, 2010.[82] M. Dickson y J. P. Gagnon, “Key factors in the rising cost of new drug discovery and development,” Nature reviews Drug discovery, vol. 3, n.o 5, págs. 417-429, 2004.[83] H. Xue, J. Li, H. Xie e Y. Wang, “Review of drug repositioning approaches and resources,” International journal of biological sciences, vol. 14, n.o 10, pág. 1232, 2018.[84] A. F. Moumbock, J. Li, P. Mishra, M. Gao y S. Günther, “Current computational methods for predicting protein interactions of natural products,” Computational and Structural Biotechnology Journal, vol. 17, págs. 1367-1376, 2019.[85] J. Fan, A. Fu y L. Zhang, “Progress in molecular docking,” Quantitative Biology, vol. 7, n.o 2, págs. 83-89, 2019.[86] A. Ezzat, M. Wu, X.-L. Li y C.-K. Kwoh, “Computational prediction of drug– target interactions using chemogenomic approaches: an empirical survey,” Brie- fings in bioinformatics, vol. 20, n.o 4, págs. 1337-1357, 2019.[87] T. Pahikkala, A. Airola, S. Pietilä y col., “Toward more realistic drug–target interaction predictions,” Briefings in bioinformatics, vol. 16, n.o 2, págs. 325-337, 2015.[88] T. Fawcett, “An introduction to ROC analysis,” Pattern recognition letters, vol. 27, n.o 8, págs. 861-874, 2006.[89] J. Davis y M. Goadrich, “The relationship between Precision-Recall and ROC curves,” en Proceedings of the 23rd international conference on Machine learning, 2006, págs. 233-240.[90] K. Abbasi, P. Razzaghi, A. Poso, S. Ghanbari-Ara y A. Masoudi-Nejad, “Deep learning in drug target interaction prediction: current and future perspectives,” Current Medicinal Chemistry, vol. 28, n.o 11, págs. 2100-2113, 2021.[91] M. Wen, Z. Zhang, S. Niu y col., “Deep-learning-based drug–target interaction prediction,” Journal of proteome research, vol. 16, n.o 4, págs. 1401-1409, 2017.[92] H. Öztürk, A. Özgür y E. Ozkirimli, “DeepDTA: deep drug–target binding affi- nity prediction,” Bioinformatics, vol. 34, n.o 17, págs. i821-i829, 2018.[93] M. Hassan, D. C. Mogollon, O. Fuentes y col., “DLSCORE: A deep learning model for predicting protein-ligand binding affinities,” 2018.[94] L. Xie, S. He, X. Song, X. Bo y Z. Zhang, “Deep learning-based transcripto- me data classification for drug-target interaction prediction,” BMC genomics, vol. 19, n.o 7, págs. 93-102, 2018.[95] Q. Feng, E. Dueva, A. Cherkasov y M. Ester, “Padme: A deep learning-based fra- mework for drug-target interaction prediction,” arXiv preprint arXiv:1807.09741, 2018.[96] M. Tsubaki, K. Tomii y J. Sese, “Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences,” Bioinforma- tics, vol. 35, n.o 2, págs. 309-318, 2019.[97] M. Karimi, D. Wu, Z. Wang e Y. Shen, “DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks,” Bioinformatics, vol. 35, n.o 18, págs. 3329-3338, 2019.[98] I. Lee, J. Keum y H. Nam, “DeepConv-DTI: Prediction of drug-target interac- tions via deep learning with convolution on protein sequences,” PLoS compu- tational biology, vol. 15, n.o 6, e1007129, 2019.[99] B. Shin, S. Park, K. Kang y J. C. Ho, “Self-attention based molecule representa- tion for predicting drug-target interaction,” en Machine Learning for Healthcare Conference, PMLR, 2019, págs. 230-248.[100] K. Huang, C. Xiao, L. M. Glass y J. Sun, “MolTrans: Molecular Interaction Transformer for drug–target interaction prediction,” Bioinformatics, vol. 37, n.o 6, págs. 830-836, 2021.[101] K. Abbasi, P. Razzaghi, A. Poso, M. Amanlou, J. B. Ghasemi y A. Masoudi- Nejad, “DeepCDA: deep cross-domain compound–protein affinity prediction th- rough LSTM and convolutional neural networks,” Bioinformatics, vol. 36, n.o 17, págs. 4633-4642, 2020.[102] Z. Yang, W. Zhong, L. Zhao y C. Y.-C. Chen, “MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction,” Chemical Science, 2022.[103] I. Lee y H. Nam, “Sequence-based prediction of protein binding regions and drug–target interactions,” Journal of cheminformatics, vol. 14, n.o 1, págs. 1-15, 2022.[104] P. Zhang, Z. Wei, C. Che y B. Jin, “DeepMGT-DTI: Transformer network incor- porating multilayer graph information for Drug–Target interaction prediction,” Computers in biology and medicine, pág. 105 214, 2022.[105] F. Cui, Z. Zhang y Q. Zou, “Sequence representation approaches for sequence- based protein prediction tasks that use deep learning,” Briefings in functional genomics, vol. 20, n.o 1, págs. 61-73, 2021.[106] X. Chen, B. Ren, M. Chen y col., “ASDCD: antifungal synergistic drug combi- nation database,” PloS one, vol. 9, n.o 1, e86499, 2014.[107] T. Liu, Y. Lin, X. Wen, R. N. Jorissen y M. K. Gilson, “BindingDB: a web- accessible database of experimentally determined protein–ligand binding affini- ties,” Nucleic acids research, vol. 35, n.o suppl_1, págs. D198-D201, 2007.[108] S. Placzek, I. Schomburg, A. Chang y col., “BRENDA in 2017: new perspectives and new tools in BRENDA,” Nucleic acids research, gkw952, 2016.[109] R. Kumar, K. Chaudhary, S. Gupta y col., “CancerDR: cancer drug resistance database,” Scientific reports, vol. 3, n.o 1, págs. 1-6, 2013.[110] K. P. Seiler, G. A. George, M. P. Happ y col., “ChemBank: a small-molecule screening and cheminformatics resource database,” Nucleic acids research, vol. 36, n.o suppl_1, págs. D351-D359, 2007.[111] D. Mendez, A. Gaulton, A. P. Bento y col., “ChEMBL: towards direct deposition of bioassay data,” Nucleic acids research, vol. 47, n.o D1, págs. D930-D940, 2019.[112] J. Kringelum, S. K. Kjaerulff, S. Brunak, O. Lund, T. I. Oprea y O. Taboureau, “ChemProt-3.0: a global chemical biology diseases mapping,” Database, vol. 2016, 2016.[113] Y. Liu, Q. Wei, G. Yu, W. Gai, Y. Li y X. Chen, “DCDB 2.0: a major update of the drug combination database,” Database, vol. 2014, 2014.[114] K. C. Cotto, A. H. Wagner, Y.-Y. Feng y col., “DGIdb 3.0: a redesign and expansion of the drug–gene interaction database,” Nucleic acids research, vol. 46, n.o D1, págs. D1068-D1073, 2018.[115] D. S. Wishart, Y. D. Feunang, A. C. Guo y col., “DrugBank 5.0: a major update to the DrugBank database for 2018,” Nucleic acids research, vol. 46, n.o D1, págs. D1074-D1082, 2018.[116] A. J. Pawson, J. L. Sharman, H. E. Benson y col., “The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands,” Nucleic acids research, vol. 42, n.o D1, págs. D1098-D1106, 2014.[117] S. Orchard, M. Ammari, B. Aranda y col., “The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases,” Nucleic acids research, vol. 42, n.o D1, págs. D358-D363, 2014.[118] M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato y K. Morishima, “KEGG: new perspectives on genomes, pathways, diseases and drugs,” Nucleic acids research, vol. 45, n.o D1, págs. D353-D361, 2017.[119] S. Günther, M. Kuhn, M. Dunkel y col., “SuperTarget and Matador: resources for exploring drug-target relationships,” Nucleic acids research, vol. 36, n.o suppl_1, págs. D919-D922, 2007.[120] J. Von Eichborn, M. S. Murgueitio, M. Dunkel, S. Koerner, P. E. Bourne y R. Preissner, “PROMISCUOUS: a database for network-based drug-repositioning,” Nucleic acids research, vol. 39, n.o suppl_1, págs. D1060-D1066, 2010.[121] S. Kim, P. A. Thiessen, E. E. Bolton y col., “PubChem substance and compound databases,” Nucleic acids research, vol. 44, n.o D1, págs. D1202-D1213, 2016.[122] M. Kuhn, M. Campillos, I. Letunic, L. J. Jensen y P. Bork, “A side effect resource to capture phenotypic effects of drugs,” Molecular systems biology, vol. 6, n.o 1, pág. 343, 2010.[123] D. Szklarczyk, A. Santos, C. Von Mering, L. J. Jensen, P. Bork y M. Kuhn, “STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data,” Nucleic acids research, vol. 44, n.o D1, págs. D380-D384, 2016.[124] J. Nickel, B.-O. Gohlke, J. Erehman y col., “SuperPred: update on drug classifi- cation and target prediction,” Nucleic acids research, vol. 42, n.o W1, W26-W31, 2014.[125] N. Hecker, J. Ahmed, J. von Eichborn y col., “SuperTarget goes quantitative: update on drug–target interactions,” Nucleic acids research, vol. 40, n.o D1, págs. D1113-D1117, 2012.[126] M. P. Magariños, S. J. Carmona, G. J. Crowther y col., “TDR Targets: a chemo- genomics resource for neglected diseases,” Nucleic acids research, vol. 40, n.o D1, págs. D1118-D1127, 2012.[127] C. Qin, C. Zhang, F. Zhu y col., “Therapeutic target database update 2014: a resource for targeted therapeutics,” Nucleic acids research, vol. 42, n.o D1, págs. D1118-D1123, 2014.[128] K. Roy y S. Kar, “The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on ‘Is regression through origin useful in external validation of QSAR models?’)” European Journal of Pharmaceutical Sciences, vol. 62, págs. 111-114, 2014.[129] A. R. Brentnall y J. Cuzick, “Use of the concordance index for predictors of censored survival data,” Statistical methods in medical research, vol. 27, n.o 8, págs. 2359-2373, 2018.[130] K. Huang, T. Fu, W. Gao y col., “Therapeutics Data Commons: Machine lear- ning datasets and tasks for drug discovery and development,” arXiv preprint arXiv:2102.09548, 2021.[131] J. Tang, A. Szwajda, S. Shakyawar y col., “Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis,” Journal of Chemical Information and Modeling, vol. 54, n.o 3, págs. 735-743, 2014.[132] R. Sennrich, B. Haddow y A. Birch, “Neural machine translation of rare words with subword units,” arXiv preprint arXiv:1508.07909, 2015.[133] S. Chithrananda, G. Grand y B. Ramsundar, “Chemberta: Large-scale self- supervised pretraining for molecular property prediction,” arXiv preprint ar- Xiv:2010.09885, 2020.[134] A. Nambiar, M. Heflin, S. Liu, S. Maslov, M. Hopkins y A. Ritz, “Transforming the language of life: Transformer neural networks for protein prediction tasks,” en Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2020, págs. 1-8.[135] M. Filipavicius, M. Manica, J. Cadow y M. R. Martinez, “Pre-training pro- tein language models with label-agnostic binding pairs enhances performance in downstream tasks,” arXiv preprint arXiv:2012.03084, 2020.[136] A. Paszke, S. Gross, F. Massa y col., “Pytorch: An imperative style, high- performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019.PublicationORIGINALRoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdfRoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdfapplication/pdf5305386https://repositorio.utp.edu.co/bitstreams/5f39c41c-516c-4e2c-8eff-7d1ab2667010/download282b6a53ccc972531fa22bf157277e74MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.utp.edu.co/bitstreams/1af2f147-9925-4389-8bba-1a8445148eeb/download73a5432e0b76442b22b026844140d683MD52THUMBNAILImagen11.pngimage/png605490https://repositorio.utp.edu.co/bitstreams/c798bf44-677d-4362-afbe-d4d20d69cad1/download90447339de12d2679cc6e0cc864b85ecMD53RoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdf.jpgRoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdf.jpgGenerated Thumbnailimage/jpeg13900https://repositorio.utp.edu.co/bitstreams/db269042-356b-41b7-9ced-66a8557530b0/downloada10543daca66a5b0006cc7939996fb56MD55TEXTRoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdf.txtRoberDTA DTI- Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo (1).pdf.txtExtracted texttext/plain104046https://repositorio.utp.edu.co/bitstreams/2ee66e9a-1750-4c49-815b-c712bfa6dd35/download39f94df051fd3a03eebe6a659c028a50MD5411059/15513oai:repositorio.utp.edu.co:11059/155132024-12-04 07:11:46.786https://creativecommons.org/licenses/by-nc-sa/4.0/Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 de la Ley 23 de 1982. En concordancia suscribo (suscribimos) este documento en el momento mismo que hago (hacemos) entrega de mi (nuestra) OBRA a la Biblioteca “Jorge Roa Martínez” de la Universidad Tecnológica de Pereira. Manifiesto (manifestamos) que la OBRA objeto de la presente autorizaciónopen.accesshttps://repositorio.utp.edu.coRepositorio de la Universidad Tecnológica de Pereirabdigital@metabiblioteca.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

RoberDTA/DTI : Arquitectura basada en Transformer para la predicción de interacción fármaco-proteína objetivo

Publicaciones similares