2. Academic Validation
  3. Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

  • J Chem Inf Model. 2023 Aug 28;63(16):5341-5355. doi: 10.1021/acs.jcim.3c00543.
Lin Wei 1 2,  Min Xu 1,  Zhiqiang Liu 1,  Chongguo Jiang 1,  Xiaohua Lin 1,  Yaogang Hu 1,  Xiaoming Wen 1,  Rongfeng Zou 1,  Chunwang Peng 1,  Hongrui Lin 1,  Guo Wang 1,  Lijun Yang 1,  Lei Fang 1,  Mingjun Yang 1,  Peiyu Zhang 1
Affiliations

Affiliations

  • 1 Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China.
  • 2 Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
PMID: 37549337 DOI: 10.1021/acs.jcim.3c00543
Abstract

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

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