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Cheng Y, Ji C, Xu J, Chen R, Guo Y, Bian Q, Shen Z, Zhang B. LCK-SafeScreen-Model: An Advanced Ensemble Machine Learning Approach for Estimating the Binding Affinity between Compounds and LCK Target. Molecules 2023; 28:7382. [PMID: 37959801 PMCID: PMC10650606 DOI: 10.3390/molecules28217382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R2 values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project.
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Affiliation(s)
- Ying Cheng
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Cong Ji
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
| | - Jun Xu
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
- Department of Pharmacy, Huzhou Central Hospital, Huzhou 313000, China
| | - Roufen Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Yu Guo
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Qingyu Bian
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Zheyuan Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Bo Zhang
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
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3D-QSAR and molecular docking studies of 4-methyl quinazoline derivatives as PI3Kα inhibitors. J INDIAN CHEM SOC 2021. [DOI: 10.1016/j.jics.2021.100183] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Ganjoo A, Prabhakar C. In silico structural anatomization of spleen tyrosine kinase inhibitors: Pharmacophore modeling, 3D QSAR analysis and molecular docking studies. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2019.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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