Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity...

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery education

Background:

• Modern drug discovery increasingly relies on computational tools.
• Quantitative structure-activity relationships (QSAR), particularly 3D QSAR, are vital for optimizing drug compounds.
• There is a need for accessible platforms to teach these complex methods.

Purpose of the Study:

• To present a novel, free, and open-source web application for teaching drug design principles.
• To integrate modern molecular modeling tools into undergraduate chemistry and pharmacy curricula.
• To provide students with hands-on experience in building 3D QSAR models.

Main Methods:

• Utilized the www.3d-qsar.com web application suite for teaching drug design.
• Developed a teaching protocol for master's students involving building ligand-based 3D QSAR models from published data.
• Employed specific tools for data set creation (Py-MolEdit), conformation generation (Py-ConfSearch), alignment (Py-Align), and model building (Py-CoMFA).

Main Results:

• Students successfully built robust 3D QSAR models, validating the usability of the web tools for inexperienced users.
• The teaching protocol effectively provided both theoretical and practical perspectives on drug design.
• The developed 3D QSAR models obtained by students were satisfactory and scientifically sound.

Conclusions:

• The www.3d-qsar.com platform offers an effective and accessible means for teaching 3D QSAR methodology.
• This approach provides undergraduates with valuable computational drug discovery experience, comparable to traditional lab work.
• The portal serves as a crucial resource for performing 3D QSAR, especially given the obsolescence of older software.