QSAR-3D as an abstraction tool in the education of medical chemistry

Authors

  • Dimas Ignacio Torres Cátedra de Química Medicinal, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires

Keywords:

Medicinal Chemistry, QSAR-3D, Graphics

Abstract

This work seeks to analyze the benefits and difficulties of implementing QSAR-3D in the teaching of medicinal chemistry. The theoretical context of both the QSAR-3D method and the pedagogical bases that underlie this educational intervention will be discussed. The most important axes are the current limitations in the students' capacity for abstraction and the new technologies that make it possible to propose a novel approach in an area of ​​great relevance in medicinal chemistry. In particular, the feasibility of conducting a computational experiment remotely is evaluated. Connections with other chemistry sub-disciplines are also revealed to highlight their value as cross-sectional content

References

Ballante, F. y Ragno, R. (2012). 3-D QSAutogrid/R: An alternative procedure to build 3-D QSAR models. Methodologies and applications. Journal of chemical information and modeling, 52(6), 1674–1685. https://doi.org/10.1021/ci300123x

Cramer, R. D., Patterson, D. E. y Bunce, J. D. (1988). Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, 110(18), 5959–5967. https://doi.org/10.1021/ja00226a005

Hansch, C., Fujita, T. (1964). p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. Journal of the American Chemical Society, 86(8), 1616–1626. https://doi.org/10.1021/ja01062a035

Johnstone, A. H. (1993). The development of chemistry teaching: a changing response to a changing demand. Journal of Chemical Education, 70(9), 701-705. https://doi.org/10.1021/ed070p701

Kubinyi, H. (1997). QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discovery Today, 2(11), 457-467. https://doi.org/10.1016/S1359-6446(97)01079-9

Lorenzo, M. G. y Pozo, J. I. (2010). La representación gráfica de la estructura espacial de las moléculas: eligiendo entre múltiples sistemas de notación. Cultura y Educación, 22(2), 231-246. https://doi.org/10.1174/113564010791304555

Ragno, R., Esposito, V., Di Mario, M., Masiello, S., Viscovo, M. y Cramer, R. (2020). Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure–Activity Relationships through Web Applications. Journal of Chemical Education, 97(7), 1922-1930. https://doi.org/10.1021/acs.jchemed.0c00117

Robinson, S. y Sigman, M. (2020). Integrating Electrochemical and Statistical Analysis Tools for Molecular Design and Mechanistic Understanding. Accounts of Chemical Research, 53(2), 289–299. https://doi.org/10.1021/acs.accounts.9b00527

Talanquer, V. A. (2011). Macro, Submicro, and Symbolic: The many faces of the chemistry “triplet”. International Journal of Science Education, 33, 179 - 195. https://doi.org/10.1080/09500690903386435

Talanquer, V. A. (2018). Progressions in reasoning about structure-property relationships. Chemical Education Research and Practice, 19, 998-1009. https://doi.org/10.1039/C7RP00187H

Published

2021-12-29

How to Cite

Torres, D. I. (2021). QSAR-3D as an abstraction tool in the education of medical chemistry. Educación En La Química, 27(02), 244–250. Retrieved from https://educacionenquimica.com.ar/index.php/edenlaq/article/view/67

Issue

Section

Innovación para la enseñanza de la Química

Similar Articles

<< < 11 12 13 14 15 16 17 18 19 > >> 

You may also start an advanced similarity search for this article.