Florian Führer, Andrea Gruber, Holger Diedam, Andreas H. Göller, Stephan Menz, Sebastian Schneckener
{"title":"深度神经网络:预测大鼠药代动力学的机理混合模型。","authors":"Florian Führer, Andrea Gruber, Holger Diedam, Andreas H. Göller, Stephan Menz, Sebastian Schneckener","doi":"10.1007/s10822-023-00547-9","DOIUrl":null,"url":null,"abstract":"<div><p>An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893–4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat\",\"authors\":\"Florian Führer, Andrea Gruber, Holger Diedam, Andreas H. Göller, Stephan Menz, Sebastian Schneckener\",\"doi\":\"10.1007/s10822-023-00547-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893–4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.</p></div>\",\"PeriodicalId\":621,\"journal\":{\"name\":\"Journal of Computer-Aided Molecular Design\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer-Aided Molecular Design\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10822-023-00547-9\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer-Aided Molecular Design","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10822-023-00547-9","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat
An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893–4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.
期刊介绍:
The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas:
- theoretical chemistry;
- computational chemistry;
- computer and molecular graphics;
- molecular modeling;
- protein engineering;
- drug design;
- expert systems;
- general structure-property relationships;
- molecular dynamics;
- chemical database development and usage.