{"title":"VisMole:基于体素的分子表征,用于分子性质预测","authors":"Qiang Tong, Jiahao Shen, Xiulei Liu","doi":"10.1117/12.2667694","DOIUrl":null,"url":null,"abstract":"To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"412 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"VisMole: a molecular representation based on voxel for molecular property prediction\",\"authors\":\"Qiang Tong, Jiahao Shen, Xiulei Liu\",\"doi\":\"10.1117/12.2667694\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.\",\"PeriodicalId\":345723,\"journal\":{\"name\":\"Fifth International Conference on Computer Information Science and Artificial Intelligence\",\"volume\":\"412 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fifth International Conference on Computer Information Science and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2667694\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Computer Information Science and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2667694","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VisMole: a molecular representation based on voxel for molecular property prediction
To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.