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

TLDR

CADD software is widely available, yet many biomedical researchers lack computational training and funding, leaving a large underserved population; student feedback provides baseline data on user experience from non‑computational researchers. The authors developed ezCADD, a web‑based CADD environment designed to be easy, quick, user‑friendly, and 2D/3D visualization‑enabled, to help researchers overcome barriers to using CADD. ezCADD implements small‑molecule docking, protein‑protein docking, and binding pocket detection, and was evaluated by deploying it as an active‑learning tool for first‑year pharmacy students in a Principles of Drug Action course. The service handled 95 simultaneous docking jobs, and a survey of 95 students showed 97 % completed experiments with minimal training, 88 % found it easy, 99–100 % reported enhanced understanding of drug‑receptor structures, and overall modeling experience improved markedly, demonstrating ezCADD’s effectiveness for research and STEM education. More advanced CADD applications are being added to ezCADD and are available at http://dxulab.org/software.

Abstract

As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled. In this paper, we describe the features of three fundamental applications that have been implemented in ezCADD: small-molecule docking, protein-protein docking, and binding pocket detection, and their applications in drug design against a pathogenic microbial enzyme as an example. To assess user experience and the effectiveness of our implementation, we introduced ezCADD to first-year pharmacy students as an active learning exercise in the Principles of Drug Action course. The web service robustly handled 95 simultaneous molecular docking jobs. Our survey data showed that among the 95 participating students, 97% completed the molecular docking experiment on their own at least partially without extensive training; 88% considered ezCADD easy and user-friendly; 99-100% agreed that ezCADD enhanced the understanding of drug-receptor structures and recognition; and the student experience in molecular modeling and visualization was significantly improved from zero to a higher level. The student feedback represents the baseline data of user experience from noncomputational researchers. It is demonstrated that in addition to supporting drug discovery research, ezCADD is also an effective tool for promoting science, technology, engineering, and mathematics (STEM) education. More advanced CADD applications are being developed and added to ezCADD, available at http://dxulab.org/software .