ezCADD: A Rapid 2D/3D Visualization-Enabled Web Modeling Environment for Democratizing Computer-Aided Drug Design.

Area of Science:

• Biomedical research
• Computational chemistry
• Drug discovery

Background:

• Computer-aided drug design (CADD) software often requires computational expertise and significant funding, limiting its accessibility.
• A substantial population of biomedical researchers lacks the necessary training and resources to utilize CADD tools effectively.
• Bridging this gap is crucial for advancing drug discovery efforts globally.

Purpose of the Study:

• To develop and present ezCADD, a web-based CADD environment designed for ease of use and accessibility.
• To implement fundamental CADD applications including small-molecule docking, protein-protein docking, and binding pocket detection within ezCADD.
• To evaluate the usability and effectiveness of ezCADD for researchers with no prior computational background.

Main Methods:

• ezCADD was developed based on principles of simplicity, speed, user-friendliness, and 2D/3D visualization.
• Core applications like molecular docking and binding pocket analysis were implemented.
• First-year pharmacy students used ezCADD as an active learning tool in a drug action course to assess user experience.

Main Results:

• The ezCADD web service successfully managed 95 simultaneous molecular docking jobs.
• A high percentage of students (97%) completed experiments with minimal training, and 88% found ezCADD easy to use.
• Students reported significant improvements in understanding drug-receptor interactions and molecular visualization.

Conclusions:

• ezCADD effectively lowers barriers to CADD, supporting drug discovery research for non-computational scientists.
• The platform serves as an effective tool for Science, Technology, Engineering, and Mathematics (STEM) education, enhancing scientific understanding.
• ezCADD demonstrates potential for broader adoption in both research and educational settings.