El impacto de la Inteligencia Artificial en las ciencias de la vida a través de la Bioinformática

Autores/as

DOI:

https://doi.org/10.35699/2965-6931.2023.47996

Palabras clave:

bioinformática, inteligencia artificial, aprendizaje automático

Resumen

En los últimos años, las técnicas de inteligencia artificial (IA) han revolucionado la investigación en las ciencias de la vida. Esto fue posible gracias al surgimiento de nuevos métodos y tecnologías que permitieron generar datos biológicos de alta calidad y a gran escala. Aliado a esto, las técnicas bioinformáticas han permitido el modelado y resolución de problemas biológicos por lo que las aplicaciones de modelos de aprendizaje automático han planteado nuevas perspectivas. En este artículo, abordaremos los impactos de la IA en las ciencias de la vida, con especial énfasis en aquellos mediados por la bioinformática, los avances en los modelos y algoritmos de IA y las consecuencias para la investigación en las ciencias de la vida.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Lucas Moraes dos Santos, Universidade Federal de Minas Gerais (UFMG)

Tiene una maestría en Bioinformática por la Universidad Federal de Minas Gerais (2022) y una licenciatura en Ingeniería Informática por la Universidad de Caxias do Sul (2019). Sus principales intereses de investigación están relacionados con la Inteligencia Artificial y la Biología Estructural. Actualmente es candidato a doctorado en el Programa de Posgrado en Bioinformática de la UFMG.

Diego Mariano, Universidade Federal de Minas Gerais (UFMG)

Residente pós-doutoral no Laboratório de Bioinformática e Sistemas da UFMG. Editor-in-chief na Revista BIOINFO e Guest Associate Editor na Frontiers in Bioinformatics. Vice coordenador da rede bioinfo.com. Revisor em diversos periódicos internacionais, como PlosOne, Molecules, Oxford Bioinformatics, dentre outros. Mestre e doutor pelo Programa de Pós-graduação em Bioinformática da UFMG. Vencedor do prêmio UFMG de teses e do Prêmio Nacional de Teses da AB3C em 2020. Tem experiência no desenvolvimento de aplicações web, visualização de dados e manipulação de bases de dados biológicas.

Raquel Cardoso de Melo-Minardi, Universidade Federal de Minas Gerais (UFMG)

possui doutorado em Bioinformática pela Universidade Federal de Minas Gerais (2008) e graduação em Ciência da Computação pela mesma instituição (2004). Realizou seu pós-doutorado no Comissariat à l'Energie Atomique et aux Énergies Alternatives / CEA na França (2008/2009). Atualmente é Professora Classe D Nível 03 (antigo Associado 3) da Universidade Federal de Minas Gerais no Departamento de Ciência da Computação. É membro afiliado da Academia Brasileira de Ciências (2019-2023). Atua como docente permanente no Programa de Pós-Graduação em Ciência da Computação (cenceito 7 da CAPES) e no Programa de Pós-Graduação em Bioinformática (conceito 7 da CAPES). É sub-coordenadora do Programa de Pós-Graduação em Bioinformática da UFMG (gestão 2020-2021 e 2022-2024), secretária da regional Centro-Sudeste da Associação Brasileira de Biologia Computacional e Bioinformática (AB3C), coordenadora do Comitê Especial de Biologia Computacional (CE-BioComp) da Sociedade Brasileira de Computação (SBC) (2022) e vice-coordenadora do mesmo comitê em 2022. Seus principais interesses de pesquisa são Bioinformática e Biologia Computacional, Inteligência Artificial e Visualização de Dados.

Citas

ABBASI, Maryam et al. Designing optimized drug candidates with Generative Adversarial Network. Journal of Cheminformatics, 14, n. 1, 2022. 40.

ALQURAISHI, Mohammed. AlphaFold at CASP13. Bioinformatics, 35, n. 22, 2019. 4862-4865.

ANAND, Namrata; HUANG, Po-Ssu. Generative modeling for protein structures. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 31, 2018. 7505-7516.

ANISHCHENKO, Ivan. et al. De novo protein design by deep network hallucination. Nature, 600, n. 7889, 2021. 547-552.

ANSTINE, Dylan M.; ISAYEV, Olexandr. Generative Models as an Emerging Paradigm in the Chemical Sciences. Journal of the American Chemical Society, 145, n. 16, 2023. 8736-8750.

BAI, Qifeng et al. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIREs Computational Molecular Science, 12, n. 3, 2021. e1581.

BENGIO, Yoshua; COURVILLE, Aaron; VINCENT, Pascal. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, n. 8, 2013. 1798-1828.

BIAN, Yuemin; XIE, Xiang-Qun. Generative chemistry: drug discovery with deep learning generative models. Journal of Molecular Modeling, 27, n. 3, 2021. 71-89.

BILODEAU, Camille et al. Generative models for molecular discovery: Recent advances and challenges. WIREs Computational Molecular Science, 12, n. 5, 2022.

BRANDES, Nadav et al. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics, 38, n. 8, 2022. 2102-2110.

BRANDES, Nadav et al. Genome-wide prediction of disease variant effects with a deep protein language model. Nature Genetics, 2023.

CALLAWAY, Ewen. AI tools are designing entirely new proteins that could transform medicine. Nature, 619, n. 7969, 2023. 236-238.

CHEN, Ziqi et al. Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models. Preprint, 2023. Disponível em: <https://arxiv.org/abs/2308.11890>. Acesso em: 5 Setembro 2023.

CHENTHAMARAKSHAN, Vijil et al. Accelerating drug target inhibitor discovery with a deep generative foundation model. Science Advances, 9, n. 25, 2023.

DEFRESNE, Marianne; BARBE, Sophie; SCHIEX, Thomas. Protein Design with Deep Learning. International Journal of Molecular Sciences, 22, n. 21, 2021. 11741.

DILL, Ken A.; MACCALLUM, Justin L. The Protein-Folding Problem, 50 Years On. Science, 338, n. 6110, 2012. 1042-1046.

DING, Wenze; GONG, Haipeng. Predicting the Real-Valued Inter-Residue Distances for Proteins. Advanced Science, 7, n. 19, 2020. 2001314.

DU, Zongyang et al. The trRosetta server for fast and accurate protein structure prediction. Nature Protocols, 16, n. 12, 2021. 5634-5651.

DUDA, Richard O.; HART, Peter E.; STORK, David G. Pattern Classification. 2ª. ed.

FERRUZ, Noelia; SCHMIDT, Steffen; HÖCKER, Birte. ProtGPT2 is a deep unsupervised language. Nature Communications, 13, n. 1, 2022. 4348.

GAO, Wenhao et al. Deep Learning in Protein Structural Modeling and Design. Patterns, 1, n. 9, 2020. 100142.

GONZALEZ, Rafael C.; WOODS, Richard E. Digital Image Processing. 4ª. ed.

GOODFELLOW, Ian J. et al. Generative Adversarial Networks. Advances in Neural Information Processing System, 2014. 2672-2680.

GOODFELLOW, Ian; BENGIO, Yoshua; COURVILLE, Aaron. Deep Learning.

HAN, Minwoo et al. Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps. Computational and Structural Biotechnology Journal, 20, 2022. 6360-6374.

HAYKIN, Simon. Neural Networks: A Comprehensive Foundation. 2ª. ed.

HOLT, Charles A.; ROTH, Alvin E. The Nash equilibrium: A perspective. Proceedings of the National Academy of Sciences, 101, n. 12, 2004. 3999-4002.

HOPFIELD, J. J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, n. 8, 1982. 2554-2558.

HUANG, Po-Ssu; BOYKEN, Scott E.; BAKER, David. The coming of age of de novo protein design. Nature, 537, n. 7620, 2016. 320-327.

JUMPER, John et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596, n. 7873, 2021. 583-589.

KINGMA, Diederik P.; WELLING, Max. Auto-Encoding Variational Bayes, 2013.

LECUN, Yan et al. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1, n. 4, 1989. 541-551.

LI, Chuan et al. An Interpretable Convolutional Neural Network Framework for Analyzing Molecular Dynamics Trajectories: a Case Study on Functional States for G-Protein-Coupled Receptors. Journal of Chemical Information and Modeling, 62, n. 6, 2022. 1399-1410.

LI, Xuanyi et al. Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors. Journal of Cheminformatics, 12, n. 1, 2020. 42.

LI, Yuesen et al. DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins. Preprint, 2023. Disponível em: <https://doi.org/10.1101/2023.06.29.543848>. Acesso em: 5 Setembro 2023.

LI, Zhaoyu et al. Protein Loop Modeling Using Deep Generative Adversarial Network. IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017. 1085-1091.

LIN, Zeming et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379, n. 6637, 2023. 1123-1130.

MAO, Jiashun et al. Application of a deep generative model produces novel and diverse functional peptides against microbial resistance. Computational and Structural Biotechnology Journal, 21, 2023. 463-471.

MARIANO, Diego et al. A Brief History of Bioinformatics Told by Data Visualization. Advances in Bioinformatics and Computational Biology. BSB 2020. Lecture Notes in Computer Science. Belo Horizonte: Springer, Cham. 2020. p. 235-246.

METHOD of the Year 2021: Protein structure prediction. Nature Methods, 19, n. 1, 2022.

MIN, Seonwoo; LEE, Byunghan; YOON, Sungroh. Deep learning in bioinformatics. Briefings in Bioinformatics, 18, n. 5, 2017. 851–869.

MISHRA, Sarbani et al. Introduction to the World of Bioinformatics. A Guide to Applied Machine Learning for Biologists, 2023. 105-126.

MITCHELL, Tom M. Machine learning.

MOLNAR, Christoph. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2ª. ed.

NASH, John F. Equilibrium points in n-person games. Proceedings of the National Academy of Sciences, 36, n. 1, 1950. 48-49.

PLANTE, Ambrose et al. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs. Molecules, 24, n. 11, 2019. 2097.

PRAVALPHRUEKUL, Nutaya et al. De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder. Journal of Chemical Information and Modeling, 63, n. 13, 2023. 3999-4011.

RAO, Roshan. et al. Transformer protein language models are unsupervised structure learners. International Conference on Learning Representations, 2020.

RIVES, Alexander. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. PNAS, 118, n. 15, 2019.

ROSSETTO, Allison; ZHOU, Wenjin. GANDALF: Peptide Generation for Drug Design Using Sequential and Structural Generative Adversarial Networks. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2020.

RUMELHART, David E.; HINTON, Geoffrey E.; WILLIAMS, Ronald J. Learning representations by back-propagating errors. Nature, 323, n. 6088, 1986. 533-536.

SANCHEZ-LENGELING, Benjamin; ASPURU-GUZIK, Alán. Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361, n. 6400, 2018. 360-365.

SANTOS, Lucas M. D. et al. Peptide-Protein Interface Classification Using Convolutional Neural Networks. Advances in Bioinformatics and Computational Biology. BSB 2023. Lecture Notes in Computer Science. Curitiba: Springer, Cham. 2023. p. 112-122.

SANTOS, Lucas M. D.; MELO-MINARDI, Raquel C. D. Identifying Large Scale Conformational Changes in Proteins Through Distance Maps and Convolutional Networks. Advances in Bioinformatics and Computational Biology. BSB 2022. Lecture Notes in Computer Science. Búzios: Springer, Cham. 2022. p. 56-67.

SENIOR, Andrew W. et al. Improved protein structure prediction using potentials from deep learning. Nature, 577, n. 7792, 2020. 706–710.

SOHL-DICKSTEIN, Jascha et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille: [s.n.]. 2015. p. 2256–2265.

SOUSA, Tiago et al. Generative Deep Learning for Targeted Compound Design. Journal of Chemical Information and Modeling, 61, n. 11, 2021. 5343-5361.

SURANA, Shraddha. et al. PandoraGAN: Generating antiviral peptides. SN COMPUT. SCI., 4, n. 607, 2023.

THOMAS, Morgan; BENDER, Andreas; GRAAF, Chris D. Integrating structure-based approaches in generative molecular design. Current Opinion in Structural Biology, 79, 2023. 102559.

TONG, Xiaochu et al. Generative Models for De Novo Drug Design. Journal of Medicinal Chemistry, 64, n. 19, 2021. 14011-14027.

TORRISI, Mirko; POLLASTRI, Gianluca; LE, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal, 18, 2020. 1301-1310.

VASWANI, Ashish et al. Attention is All you Need. Advances in Neural Information Processing Systems, 30, 2017. 6000–6010.

VERLI, Hugo. Bioinformática: da biologia à flexibilidade molecular.

VERT, Jean-Philippe. How will generative AI disrupt data science in

drug discovery?. Nature Biotechnology, 41, n. 6, 2023. 750-751.

WATSON, Joseph L. et al. De novo design of protein structure and function with RFdiffusion. Nature, 620, n. 7976, 2023. 1089-1100.

XIE, Xuezhi; VALIENTE, Pedro A.; KIM, Philip M. HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures. Bioinformatics, 39, n. 1, 2023.

ZENG, Xiangxiang et al. Deep generative molecular design reshapes drug discovery. Cell Reports Medicine, 3, n. 12, 2022. 100794.

ZHANG, Shuang et al. Applications of transformer-based language models in bioinformatics: a survey. Bioinformatics Advances, 3, n. 1, 2023.

Publicado

2023-12-07

Cómo citar

SANTOS, L. M. dos; MARIANO, D.; MELO-MINARDI, R. C. de. El impacto de la Inteligencia Artificial en las ciencias de la vida a través de la Bioinformática . Revista da Universidade Federal de Minas Gerais, Belo Horizonte, v. 30, n. fluxo contínuo, 2023. DOI: 10.35699/2965-6931.2023.47996. Disponível em: https://periodicos.ufmg.br/index.php/revistadaufmg/article/view/47996. Acesso em: 21 nov. 2024.