CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Xiaodan Yin, Xiaorui Wang, Yuquan Li, Jike Wang, Yuwei Wang, Yafeng Deng, Tingjun Hou, Huanxiang Liu, Pei Luo, Xiaojun Yao

研究成果: Article同行評審


Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at

頁(從 - 到)6169-6176
期刊Journal of Chemical Information and Modeling
出版狀態Published - 23 10月 2023


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