WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm

Bai, Qifeng; Ma, Jian; Liu, Shuo; Xu, Tingyang; Banegas-Luna, Antonio Jesús; Pérez-Sánchez, Horacio; Tian, Yanan; Huang, Junzhou; Liu, Huanxiang; Yao, Xiaojun

Artificial intelligence can train the related known drug data into deep learning models for drug design, while classical algorithms can design drugs through established and predefined procedures. Both deep learning and classical algorithms have their merits for drug design. Here, the webserver WADDAICA is built to employ the advantage of deep learning model and classical algorithms for drug design. The WADDAICA mainly contains two modules. In the first module, WADDAICA provides deep learning models for scaffold hopping of compounds to modify or design new novel drugs. The deep learning model which is used in WADDAICA shows a good scoring power based on the PDBbind database. In the second module, WADDAICA supplies functions for modifying or designing new novel drugs by classical algorithms. WADDAICA shows better Pearson and Spearman correlations of binding affinity than Autodock Vina that is considered to have the best scoring power. Besides, WADDAICA supplies a friendly and convenient web interface for users to submit drug design jobs. We believe that WADDAICA is a useful and effective tool to help researchers to modify or design novel drugs by deep learning models and classical algorithms. WADDAICA is free and accessible at https://bqflab.github.io or https://heisenberg.ucam.edu:5000.