化学空间
化学
预测能力
机器学习
人工智能
药物发现
计算机科学
生物化学
认识论
哲学
作者
Yanrui Suo,Qian Xu,Zhaoping Xiong,Xiaohong Liu,Chao Wang,Baiyang Mu,Xinyuan Wu,Weiwei Lu,Meiying Cui,Jiaxiang Liu,Yujie Chen,Mingyue Zheng,Xiaojie Lu
标识
DOI:10.1021/acs.jmedchem.4c01416
摘要
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recognized by human analysis. Lead compounds from DEL screens often have higher molecular weights, posing challenges for drug development. This study refines traditional DELs by integrating alternative techniques like photocross-linking screening to enhance chemical diversity. Combining these methods improved predictive performance for small molecule identification models. Using this approach, we predicted active small molecules for BRD4 and p300, achieving hit rates of 26.7 and 35.7%. Notably, the identified compounds exhibit smaller molecular weights and better modification potential compared to traditional DEL molecules. This research demonstrates the synergy between DEL and AI technologies, enhancing drug discovery.
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