LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP
文献类型：期刊论文
| 作者 | Wang, Yitian1,2; Xiong, Jiacheng1,2; Xiao, Fu1,3; Zhang, Wei1,2; Cheng, Kaiyang1,3; Rao, Jingxin1,2; Niu, Buying1,2; Tong, Xiaochu1,2; Qu, Ning1,2; Zhang, Runze1,2 |
| 刊名 | JOURNAL OF CHEMINFORMATICS
 |
| 出版日期 | 2023-09-05 |
| 卷号 | 15期号:1页码:13 |
| ISSN号 | 1758-2946 |
| 关键词 | logD7.4 Lipid solubility Graph neural network Molecular property prediction |
| DOI | 10.1186/s13321-023-00754-4 |
| 通讯作者 | Li, Xutong(lixutong@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn) |
| 英文摘要 | Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios. |
| WOS关键词 | NEURAL-NETWORKS ; LIPOPHILICITY ; PLATFORM ; DATASET |
| 资助项目 | None. |
| WOS研究方向 | Chemistry ; Computer Science |
| 语种 | 英语 |
| 出版者 | BMC |
| WOS记录号 | WOS:001060049100001 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/307064]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Li, Xutong; Zheng, Mingyue |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.Nanjing Univ Chinese Med, 138 Xianlin Rd, Nanjing 210023, Peoples R China 4.Lingang Lab, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yitian,Xiong, Jiacheng,Xiao, Fu,et al. LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP[J]. JOURNAL OF CHEMINFORMATICS,2023,15(1):13. |
| APA | Wang, Yitian.,Xiong, Jiacheng.,Xiao, Fu.,Zhang, Wei.,Cheng, Kaiyang.,...&Zheng, Mingyue.(2023).LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.JOURNAL OF CHEMINFORMATICS,15(1),13. |
| MLA | Wang, Yitian,et al."LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP".JOURNAL OF CHEMINFORMATICS 15.1(2023):13. |
入库方式： OAI收割
来源：上海药物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。