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Béquignon, O. J. M., Gómez-Tamayo, J. C., Lenselink, E. B., Wink, S., Hiemstra, S., Lam, C. C., . . . van Westen, G. J. P. (2023). Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells. Journal of Chemical Information and Modeling, 63(17), 5433-5445
Open this publication in new window or tab >>Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells
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2023 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 63, no 17, p. 5433-5445Article in journal (Refereed) Published
Abstract [en]

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2023
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:oru:diva-107840 (URN)10.1021/acs.jcim.3c00220 (DOI)001063562000001 ()37616385 (PubMedID)2-s2.0-85169891068 (Scopus ID)
Funder
EU, Horizon 2020, 681002 964537 777365
Note

Funding agencies:

EFPIA

Netherlands Organization for Scientific Research (NWO) NWA-ORC 1292.19.272

 

Available from: 2023-08-25 Created: 2023-08-25 Last updated: 2024-01-16Bibliographically approved
Sapounidou, M., Norinder, U. & Andersson, P. L. (2023). Predicting Endocrine Disruption Using Conformal Prediction: A Prioritization Strategy to Identify Hazardous Chemicals with Confidence. Chemical Research in Toxicology, 36(1), 53-65
Open this publication in new window or tab >>Predicting Endocrine Disruption Using Conformal Prediction: A Prioritization Strategy to Identify Hazardous Chemicals with Confidence
2023 (English)In: Chemical Research in Toxicology, ISSN 0893-228X, E-ISSN 1520-5010, Vol. 36, no 1, p. 53-65Article in journal (Refereed) Published
Abstract [en]

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2023
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:oru:diva-102811 (URN)10.1021/acs.chemrestox.2c00267 (DOI)000903383200001 ()36534483 (PubMedID)2-s2.0-85144410434 (Scopus ID)
Funder
European Commission, 825759 825489Swedish Foundation for Strategic Research, DIA 2018/11
Available from: 2022-12-20 Created: 2022-12-20 Last updated: 2024-01-16Bibliographically approved
Norinder, U. & Lowry, S. (2023). Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction. Remote Sensing Letters, 14(10), 1023-1035
Open this publication in new window or tab >>Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction
2023 (English)In: Remote Sensing Letters, ISSN 2150-704X, E-ISSN 2150-7058, Vol. 14, no 10, p. 1023-1035Article in journal (Refereed) Published
Abstract [en]

This investigation shows that successful forecasting models for monitoring forest health status with respect to Larch Casebearer damages can be derived using a combination of a confidence predictor framework (Conformal Prediction) in combination with a deep learning architecture (Yolo v5). A confidence predictor framework can predict the current types of diseases used to develop the model and also provide indication of new, unseen, types or degrees of disease. The user of the models is also, at the same time, provided with reliable predictions and a well-established applicability domain for the model where such reliable predictions can and cannot be expected. Furthermore, the framework gracefully handles class imbalances without explicit over- or under-sampling or category weighting which may be of crucial importance in cases of highly imbalanced datasets. The present approach also provides indication of when insufficient information has been provided as input to the model at the level of accuracy (reliability) need by the user to make subsequent decisions based on the model predictions.

Place, publisher, year, edition, pages
Taylor & Francis, 2023
Keywords
Yolo network, Larch Casebearer moth, conformal prediction, forest health, tree damage
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-108845 (URN)10.1080/2150704X.2023.2258460 (DOI)001071044000001 ()2-s2.0-85171885925 (Scopus ID)
Funder
Swedish Research Council, 2018-03807
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2024-01-16Bibliographically approved
Alijagic, A., Scherbak, N., Kotlyar, O., Karlsson, P., Persson, A., Hedbrant, A., . . . Engwall, M. (2022). Cell Painting unveils cell response signatures to (nano)particles formed in additive manufacturing. Paper presented at XVIth International Congress of Toxicology (ICT 2022) - UNITING IN TOXICOLOGY, Maastricht, The Netherlands, September 18-21, 2022. Toxicology Letters, 368(Suppl. 1), S226-S227, Article ID P17-01.
Open this publication in new window or tab >>Cell Painting unveils cell response signatures to (nano)particles formed in additive manufacturing
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2022 (English)In: Toxicology Letters, ISSN 0378-4274, E-ISSN 1879-3169, P17-01, Vol. 368, no Suppl. 1, p. S226-S227, article id P17-01Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2022
National Category
Environmental Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:oru:diva-101516 (URN)10.1016/j.toxlet.2022.07.611 (DOI)000853725600549 ()
Conference
XVIth International Congress of Toxicology (ICT 2022) - UNITING IN TOXICOLOGY, Maastricht, The Netherlands, September 18-21, 2022
Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-01-16Bibliographically approved
Tuerkova, A., Bongers, B. J., Norinder, U., Ungvári, O., Székely, V., Tarnovskiy, A., . . . Zdrazil, B. (2022). Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening. Journal of Chemical Information and Modeling, 62(24), 6323-6335
Open this publication in new window or tab >>Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
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2022 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 62, no 24, p. 6323-6335Article in journal (Refereed) Published
Abstract [en]

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2022
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:oru:diva-98150 (URN)10.1021/acs.jcim.1c01460 (DOI)000840962400001 ()35274943 (PubMedID)2-s2.0-85126619146 (Scopus ID)
Available from: 2022-03-21 Created: 2022-03-21 Last updated: 2024-01-16Bibliographically approved
Escher, S. E., Aguayo-Orozco, A., Benfenati, E., Bitsch, A., Braunbeck, T., Brotzmann, K., . . . Fisher, C. (2022). Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action. Toxicology in Vitro, 79, Article ID 105269.
Open this publication in new window or tab >>Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action
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2022 (English)In: Toxicology in Vitro, ISSN 0887-2333, E-ISSN 1879-3177, Vol. 79, article id 105269Article in journal (Refereed) Published
Abstract [en]

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity.

Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis.

To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues.

Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds.

The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE “lipid accumulation”. KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity.

Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays.

The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
AOP-network, Liver steatosis, Mechanistic hazard assessment, NAM, Read-across
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:oru:diva-95389 (URN)10.1016/j.tiv.2021.105269 (DOI)000747784000002 ()34757180 (PubMedID)2-s2.0-85121229511 (Scopus ID)
Funder
EU, Horizon 2020, 681002European Commission, PI18/00993 CP16/00097
Note

Funding agencies:

Instituto de Salud Carlos III

IATA Case Studies project ENV/JM/WRPR(2020)31 

Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2024-01-16Bibliographically approved
Morger, A., Garcia de Lomana, M., Norinder, U., Svensson, F., Kirchmair, J., Mathea, M. & Volkamer, A. (2022). Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data. Scientific Reports, 12(1), Article ID 7244.
Open this publication in new window or tab >>Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data
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2022 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 12, no 1, article id 7244Article in journal (Refereed) Published
Abstract [en]

Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable. In cases where the test data have drifted away from the descriptor space of the training data, or where assay setups have changed, this assumption might not be fulfilled and the models are not guaranteed to be valid. In this study, the performance of internally valid CP models when applied to either newer time-split data or to external data was evaluated. In detail, temporal data drifts were analysed based on twelve datasets from the ChEMBL database. In addition, discrepancies between models trained on publicly-available data and applied to proprietary data for the liver toxicity and MNT in vivo endpoints were investigated. In most cases, a drastic decrease in the validity of the models was observed when applied to the time-split or external (holdout) test sets. To overcome the decrease in model validity, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Updating the calibration set generally improved the validity, restoring it completely to its expected value in many cases. The restored validity is the first requisite for applying the CP models with confidence. However, the increased validity comes at the cost of a decrease in model efficiency, as more predictions are identified as inconclusive. This study presents a strategy to recalibrate CP models to mitigate the effects of data drifts. Updating the calibration sets without having to retrain the model has proven to be a useful approach to restore the validity of most models.

Place, publisher, year, edition, pages
Nature Publishing Group, 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-98864 (URN)10.1038/s41598-022-09309-3 (DOI)000790941900035 ()35508546 (PubMedID)2-s2.0-85129425620 (Scopus ID)
Available from: 2022-05-06 Created: 2022-05-06 Last updated: 2024-01-16Bibliographically approved
Sapounidou, M., Norinder, U. & Andersson, P. L. (2021). Application of conformal prediction for in silico definition of molecular initiating events linked to endocrine disruption. Paper presented at 56th Congress of the European Societies of Toxicology (EUROTOX 2021), Virtual Congress, September 27 – October 1, 2021. Toxicology Letters, 350(Suppl.), S86-S86
Open this publication in new window or tab >>Application of conformal prediction for in silico definition of molecular initiating events linked to endocrine disruption
2021 (English)In: Toxicology Letters, ISSN 0378-4274, E-ISSN 1879-3169, Vol. 350, no Suppl., p. S86-S86Article in journal, Meeting abstract (Other academic) Published
Abstract [en]

The adverse outcome pathway (AOP) paradigm has brought mechanism of action in the spotlight of regulatory toxicology, linking biochemical interactions on cellular level (i.e. molecular initiating event, MIE) via key events to adverse outcomes (AOs) on population level. Developments on mechanistic understanding of endocrine disruption (ED) has brought forward MIEs associated with early neurode-velopmental interference  [1] and metabolic disruption [2], describing agonistic and antagonistic interactions with receptors such as constitutive androstane receptor (CAR), estrogen receptor alpha (ERα), farsenoid X receptor (FXR), and glucocorticoid receptor (GR). High confidence on in silico predictions is dictated by high quality training data  on  mechanistically  relevant  endpoints,  where  well-defined  chemistry is covered. Based on Tox21 in vitro assays describing events of agonism and antagonism for 13 receptors linked to ED, 23 in silico models were developed using Random Forest Classification. To quantify measures of uncertainty per prediction a Conformal Prediction framework was employed. In order to assess whether currently available models can confidently predict endocrine disrupting chemicals (EDCs), screening of EURION reference chemicals was conducted. The EURION cluster is a constellation of 8 research consortia aiming to improve endocrine disruption identification. Preliminary results revealed strengths in the use of in silico models for screening of current ED chemical landscape, and data gaps that need to be considered for next steps.

Place, publisher, year, edition, pages
Elsevier, 2021
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:oru:diva-95627 (URN)000714098000219 ()
Conference
56th Congress of the European Societies of Toxicology (EUROTOX 2021), Virtual Congress, September 27 – October 1, 2021
Funder
EU, Horizon 2020, 825759
Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-16Bibliographically approved
Morger, A., Svensson, F., Arvidsson McShane, S., Gauraha, N., Norinder, U., Spjuth, O. & Volkamer, A. (2021). Assessing the calibration in toxicological in vitro models with conformal prediction. Journal of Cheminformatics, 13(1), Article ID 35.
Open this publication in new window or tab >>Assessing the calibration in toxicological in vitro models with conformal prediction
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2021 (English)In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 13, no 1, article id 35Article in journal (Refereed) Published
Abstract [en]

Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy-exchanging the calibration data only-is convenient as it does not require retraining of the underlying model.

Place, publisher, year, edition, pages
BioMed Central, 2021
Keywords
Applicability domain, Calibration plots, Conformal prediction, Data drifts, Tox21 datasets, Toxicity prediction
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:oru:diva-91683 (URN)10.1186/s13321-021-00511-5 (DOI)000645643800001 ()33926567 (PubMedID)2-s2.0-85105178991 (Scopus ID)
Funder
Swedish Research Council Formas, 2018-00924Swedish Research Council, 2020-03731 2020-01865Swedish Foundation for Strategic Research , BD150008
Note

Funding Agencies:

Projekt DEAL  

FUBright Mobility Allowances  

HaVo-Stiftung  

Federal Ministry of Education & Research (BMBF) 031A262C

Alzheimer's Research UK (ARUK) 560832

Available from: 2021-05-10 Created: 2021-05-10 Last updated: 2024-01-16Bibliographically approved
Garcia de Lomana, M., Morger, A., Norinder, U., Buesen, R., Landsiedel, R., Volkamer, A., . . . Mathea, M. (2021). ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities. Journal of Chemical Information and Modeling, 61(7), 3255-3272
Open this publication in new window or tab >>ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities
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2021 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 61, no 7, p. 3255-3272Article in journal (Refereed) Published
Abstract [en]

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 bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2021
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:oru:diva-92562 (URN)10.1021/acs.jcim.1c00451 (DOI)000678396200008 ()34153183 (PubMedID)2-s2.0-85110263600 (Scopus ID)
Note

Funding agencies:

Federal Ministry of Education & Research (BMBF) 031A262C  

HaVoStiftung

BASF

Available from: 2021-06-23 Created: 2021-06-23 Last updated: 2024-01-16Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3107-331x

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