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  1. Article: In Silico

    Andersson, Martin / Norinder, Ulf / Chavan, Swapnil / Cotgreave, Ian

    Alternatives to laboratory animals : ATLA

    2023  Volume 51, Issue 3, Page(s) 204–209

    Abstract: ... ...

    Abstract An
    MeSH term(s) Animals ; Eye ; Toxicity Tests ; Solubility ; Irritants/toxicity ; Animal Testing Alternatives
    Chemical Substances Irritants
    Language English
    Publishing date 2023-05-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 605800-0
    ISSN 0261-1929
    ISSN 0261-1929
    DOI 10.1177/02611929231175676
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3391: Show issues Location:
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  2. Article ; Online: Predicting Endocrine Disruption Using Conformal Prediction - A Prioritization Strategy to Identify Hazardous Chemicals with Confidence.

    Sapounidou, Maria / Norinder, Ulf / Andersson, Patrik L

    Chemical research in toxicology

    2022  Volume 36, Issue 1, Page(s) 53–65

    Abstract: 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 ...

    Abstract 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
    MeSH term(s) Humans ; Hazardous Substances/toxicity ; Quantitative Structure-Activity Relationship ; Molecular Conformation ; Endocrine Disruptors/toxicity
    Chemical Substances Hazardous Substances ; Endocrine Disruptors
    Language English
    Publishing date 2022-12-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 639353-6
    ISSN 1520-5010 ; 0893-228X
    ISSN (online) 1520-5010
    ISSN 0893-228X
    DOI 10.1021/acs.chemrestox.2c00267
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques.

    Ylipää, Erik / Chavan, Swapnil / Bånkestad, Maria / Broberg, Johan / Glinghammar, Björn / Norinder, Ulf / Cotgreave, Ian

    Current research in toxicology

    2023  Volume 5, Page(s) 100121

    Abstract: The rise of artificial intelligence (AI) based algorithms has gained a lot of interest in the pharmaceutical development field. Our study demonstrates utilization of traditional machine learning techniques such as random forest (RF), support-vector ... ...

    Abstract The rise of artificial intelligence (AI) based algorithms has gained a lot of interest in the pharmaceutical development field. Our study demonstrates utilization of traditional machine learning techniques such as random forest (RF), support-vector machine (SVM), extreme gradient boosting (XGBoost), deep neural network (DNN) as well as advanced deep learning techniques like gated recurrent unit-based DNN (GRU-DNN) and graph neural network (GNN), towards predicting human ether-á-go-go related gene (hERG) derived toxicity. Using the largest hERG dataset derived to date, we have utilized 203,853 and 87,366 compounds for training and testing the models, respectively. The results show that GNN, SVM, XGBoost, DNN, RF, and GRU-DNN all performed well, with validation set AUC ROC scores equals 0.96, 0.95, 0.95, 0.94, 0.94 and 0.94, respectively. The GNN was found to be the top performing model based on predictive power and generalizability. The GNN technique is free of any feature engineering steps while having a minimal human intervention. The GNN approach may serve as a basis for comprehensive automation in predictive toxicology. We believe that the models presented here may serve as a promising tool, both for academic institutes as well as pharmaceutical industries, in predicting hERG-liability in new molecular structures.
    Language English
    Publishing date 2023-09-01
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2666-027X
    ISSN (online) 2666-027X
    DOI 10.1016/j.crtox.2023.100121
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  4. Article: Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning.

    Norinder, Ulf / Spjuth, Ola / Svensson, Fredrik

    Journal of cheminformatics

    2021  Volume 13, Issue 1, Page(s) 77

    Abstract: Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal ... ...

    Abstract Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.
    Language English
    Publishing date 2021-10-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2486539-4
    ISSN 1758-2946
    ISSN 1758-2946
    DOI 10.1186/s13321-021-00555-7
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  5. Article ; Online: Deep Learning-Based Conformal Prediction of Toxicity.

    Zhang, Jin / Norinder, Ulf / Svensson, Fredrik

    Journal of chemical information and modeling

    2021  Volume 61, Issue 6, Page(s) 2648–2657

    Abstract: Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately ... ...

    Abstract Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.
    MeSH term(s) Deep Learning ; Humans ; Machine Learning ; Molecular Conformation ; Neural Networks, Computer ; Uncertainty
    Language English
    Publishing date 2021-05-27
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.1c00208
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
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  6. Article ; Online: Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction.

    Norinder, Ulf / Svensson, Fredrik

    Journal of chemical information and modeling

    2019  Volume 59, Issue 4, Page(s) 1598–1604

    Abstract: Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach ... ...

    Abstract Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach for large scale bioactivity prediction. This approach handles both high degrees of missing data and label imbalances while still producing high quality predictive models. When applied to ten assay end points from PubChem, the models generated valid models with an efficiency of 74.0-80.1% at the 80% confidence level with similar performance both for the minority and majority class. Also when deleting progressively larger portions of the available data (0-80%) the performance of the models remained robust with only minor deterioration (reduction in efficiency between 5 and 10%). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced data sets.
    MeSH term(s) Computer Simulation ; Databases, Chemical ; Informatics/methods
    Language English
    Publishing date 2019-04-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.9b00027
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
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  7. Article ; Online: QSAR Models for Predicting Five Levels of Cellular Accumulation of Lysosomotropic Macrocycles.

    Norinder, Ulf / Munic Kos, Vesna

    International journal of molecular sciences

    2019  Volume 20, Issue 23

    Abstract: Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue ... ...

    Abstract Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure-activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (M
    MeSH term(s) Chromatography, Liquid ; Computational Biology/methods ; Lysosomes/chemistry ; Macrocyclic Compounds/chemistry ; Macrocyclic Compounds/pharmacokinetics ; Molecular Structure ; Quantitative Structure-Activity Relationship ; Tandem Mass Spectrometry ; Tissue Distribution
    Chemical Substances Macrocyclic Compounds
    Language English
    Publishing date 2019-11-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms20235938
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  8. Article ; Online: Correction to "Using Predicted Bioactivity Profiles to Improve Predictive Modeling".

    Norinder, Ulf / Spjuth, Ola / Svensson, Fredrik

    Journal of chemical information and modeling

    2020  Volume 60, Issue 12, Page(s) 6722

    Language English
    Publishing date 2020-11-24
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.0c01327
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Using Predicted Bioactivity Profiles to Improve Predictive Modeling.

    Norinder, Ulf / Spjuth, Ola / Svensson, Fredrik

    Journal of chemical information and modeling

    2020  Volume 60, Issue 6, Page(s) 2830–2837

    Abstract: Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete ... ...

    Abstract Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the
    MeSH term(s) Molecular Conformation
    Language English
    Publishing date 2020-05-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.0c00250
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  10. Article ; Online: Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

    Ulf Norinder / Ola Spjuth / Fredrik Svensson

    Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-

    2021  Volume 11

    Abstract: Abstract Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of ... ...

    Abstract Abstract Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.
    Keywords Conformal prediction ; Federated learning ; Confidence ; Machine learning ; Information technology ; T58.5-58.64 ; Chemistry ; QD1-999
    Subject code 539
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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