التفاصيل البيبلوغرافية
| العنوان: |
ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities |
| المؤلفون: |
Marina Garcia de Lomana (5578643), Andrea Morger (10726702), Ulf Norinder (827452), Roland Buesen (9756835), Robert Landsiedel (2630344), Andrea Volkamer (1444000), Johannes Kirchmair (490109), Miriam Mathea (4331506) |
| سنة النشر: |
2021 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Biochemistry, Molecular Biology, Pharmacology, Immunology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, vivo endpoints, Predicted Bioactivities Computation., MNT, vivo toxicity endpoints, DILI, Enhancing Conformal Prediction, conformal prediction framework, bioactivity descriptors, DICC, bioactivity assay outcomes, bioactivity CP models, vivo toxicity data sets, F 1 scores, chemical, vivo toxicity CP models, vivo toxicological endpoints, conformal prediction models |
| الوصف: |
Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important ... |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/ChemBioSim_Enhancing_Conformal_Prediction_of_In_Vivo_Toxicity_by_Use_of_Predicted_Bioactivities/14818525 |
| DOI: |
10.1021/acs.jcim.1c00451.s003 |
| الاتاحة: |
https://doi.org/10.1021/acs.jcim.1c00451.s003 |
| Rights: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.84F7181 |
| قاعدة البيانات: |
BASE |