| Computer-aided drug design (CADD) is an indispensable part of drug discovery, which provides advanced techniques, for example, molecular modeling and high throughput virtual screening. Applying CADD techniques can significantly reduce the time and cost in the study of drug design, and impressively improve the success rate in there search and development of the drug. However, many experimental researchers in the research area of drug discovery have limited experience of CADD applications and very basic understandings of CADD, due to the expenses of CADD software packages and hardware, the complexity of CADD techniques, and the difficulty in
learning and training for CADD techniques. To eliminate the technical and fiscal barriers for underserved researchers who want to apply CADD to their studies, we have developed a web-based CADD environment (ezCADD) which provides graphical CADD applications. ezCADD simplifies the complexity of CADD, which enables researchers to perform CADD tasks without the distress of the requirements of (1) computational background; (2) computer hardware and software purchase; (3) software compilation, installation, and update; and (4) computer system maintenance. To date, we have developed several fundamental CADD applications for the ezCADD platform, for example,s mall-molecule docking (ezSMDock), binding site detection (ezPocket),protein-protein docking (ezPPDock),high throughput virtual screening (ezHTVS),drug target and poly-pharmacology identification (ezPocketSearch)and other applications. Those applications have drawn
xix researchers’ attention from all over the world. More than 20,000 CADD jobs have been executed on the ezCADD platform by more than 2000 users from different countries. A variety of CADD techniques have been applied in TMC1 and Mcs1 studies, along with experimental studies from collaborators. In the TMC1 study, we built a homology model of zebrafish TMC1 protein. Then molecular dynamics simulations and high throughput virtual screening were performed with the homology model, and machine learning and QSAR methods were also applied with the existing compound data. In the Mcs1 study, a high-quality Mcs1 protein model and a full-length VCAM-1 model were built. Molecular dynamics simulations were performed with the homology models. Then the very plausible conformations of the Mcs1 and VCAM-1 were extracted from simulation trajectories for docking studies. Key Words: Computer Aided Drug Discovery, Drug Design, Molecular Dynamics, Homology Modeling, Machine Learning, High Throughput Virtual Screening. |