Abstract

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure–activity relationships (QSAR)—often as its 3D QSAR variant—are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Highlights

  • A basic knowledge of pharmaceutical chemistry is one fundamental goal for students in master’s degree (MD) courses such as Pharmaceutical Biotechnology (PB, PBMD) or Medicinal Chemistry (MC, MCMD) and Pharmaceutical Technology (Industrial Pharmacy Degree, PT, PTMD)

  • We believe we have demonstrated the effectiveness and the convenience of the free Web site platform www.3d-qsar.com as a tool to teach 3D quantitative structure−activity relationships (QSAR) in drug design (DD) courses, including the difficult underlying chemical concepts

  • Our experiences suggest that the 3D QSAR approach is quite appropriate for undergraduate students of pharmaceutical biotechnology, and more generally as a part of bachelor, master, and Ph.D. degree programs, and perhaps even within high school courses

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Summary

■ INTRODUCTION

A basic knowledge of pharmaceutical chemistry is one fundamental goal for students in master’s degree (MD) courses such as Pharmaceutical Biotechnology (PB, PBMD) or Medicinal Chemistry (MC, MCMD) and Pharmaceutical Technology (Industrial Pharmacy Degree, PT, PTMD). Calculation of MIF Fields and Model Generation through the Web Portal For each such aligned training set, a 3D QSAR model can be generated using the Py-CoMFA module[70] with the default settings, which can be further explored by varying the probe type, the grid spacing, the minmax cutoff energy and the minimum sigma. Since the main goal of any 3D QSAR model is to predict unknown molecules activities by means of their structures and the alignment rules, an external test set is usually prepared along with the training set. Each of the four final models were further analyzed by means of classical CoMFA contour maps revealing the possibility for future drug design

■ CONCLUSIONS
■ ACKNOWLEDGMENTS
Findings
■ REFERENCES
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