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  • 1. Benfenati, E.
    et al.
    Golbamaki, A.
    Raitano, G.
    Roncaglioni, A.
    Manganelli, S.
    Lemke, F.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Sweden.
    Lo Piparo, Elena
    Honma, M.
    Manganaro, A.
    Gini, G.
    A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity($)2018In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 29, no 8, p. 591-611Article in journal (Refereed)
    Abstract [en]

    Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.

  • 2. Forreryd, Andy
    et al.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Lindberg, Tim
    Lindstedt, Malin
    Predicting skin sensitizers with confidence - Using conformal prediction to determine applicability domain of GARD2018In: Toxicology in Vitro, ISSN 0887-2333, E-ISSN 1879-3177, Vol. 48, p. 179-187Article in journal (Refereed)
    Abstract [en]

    GARD - Genomic Allergen Rapid Detection is a cell based alternative to animal testing for identification of skin sensitizers. The assay is based on a biomarker signature comprising 200 genes measured in an in vitro model of dendritic cells following chemical stimulations, and consistently reports predictive performances similar to 90% for classification of external test sets. Within the field of in vitro skin sensitization testing, definition of applicability domain is often neglected by test developers, and assays are often considered applicable across the entire chemical space. This study complements previous assessments of model performance with an estimate of confidence in individual classifications, as well as a statistically valid determination of the applicability domain for the GARD assay. Conformal prediction was implemented into current GARD protocols, and a large external test dataset (n = 70) was classified at a confidence level of 85%, to generate a valid model with a balanced accuracy of 88%, with none of the tested chemical reactivity domains identified as outside the applicability domain of the assay. In conclusion, results presented in this study complement previously reported predictive performances of GARD with a statistically valid assessment of uncertainty in each individual prediction, thus allowing for classification of skin sensitizers with confidence.

  • 3. Honma, Masamitsu
    et al.
    Kitazawa, Airi
    Cayley, Alex
    Williams, Richard V.
    Barber, Chris
    Hanser, Thierry
    Saiakhov, Roustem
    Chakravarti, Suman
    Myatt, Glenn J.
    Cross, Kevin P.
    Benfenati, Emilio
    Raitano, Giuseppa
    Mekenyan, Ovanes
    Petkov, Petko
    Bossa, Cecilia
    Benigni, Romualdo
    Battistelli, Chiara Laura
    Giuliani, Alessandro
    Tcheremenskaia, Olga
    DeMeo, Christine
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Koga, Hiromi
    Jose, Ciloy
    Jeliazkova, Nina
    Kochev, Nikolay
    Paskaleva, Vesselina
    Yang, Chihae
    Daga, Pankaj R.
    Clark, Robert D.
    Rathman, James
    Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 3-16Article in journal (Refereed)
    Abstract [en]

    The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

  • 4. Jesús Naveja, J.
    et al.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Mucs, Daniel
    López-López, Edgar
    Medina-Franco, Jose L.
    Chemical space, diversity and activity landscape analysis of estrogen receptor binders2018In: RSC Advances, ISSN 2046-2069, E-ISSN 2046-2069, Vol. 8, no 67, p. 38229-38237Article in journal (Refereed)
    Abstract [en]

    Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.

  • 5. Lindh, Martin
    et al.
    Karlen, Anders
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework2017In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 14, no 5, p. 1571-1576Article in journal (Refereed)
    Abstract [en]

    Skin serves as a drug administration route, and skin permeability of chemicals is of significant interest in the pharmaceutical and cosmetic industries. An aggregated conformal prediction (ACP) framework was used to build models, for predicting the permeation rate (log K-p) of chemical compounds through human skin. The conformal prediction method gives as an output the prediction range at a given level of confidence for each compound, which enables the user to make a more informed decision when, for example, suggesting the next compound to prepare, Predictive models were built using;both the random forest and the support vector machine methods and were based on experimentally derived permeability data on 211 diverse compounds. The derived models were of similar predictive quality as compared to earlier published models but have the extra advantage of not only presenting a single predicted value for each, compound but also a reliable, individually assigned prediction range. The models use calculated descriptors and can quickly predict the skin permeation rate of new compounds.

  • 6. Lupu, Diana
    et al.
    Varshney, Mukesh K.
    Mucs, Daniel
    Inzunza, Jose
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Loghin, Felicia
    Nalvarte, Ivan
    Ruegg, Joelle
    Fluoxetine Affects Differentiation of Midbrain Dopaminergic Neurons In Vitro2018In: Molecular Pharmacology, ISSN 0026-895X, E-ISSN 1521-0111, Vol. 94, no 4, p. 1220-1231Article in journal (Refereed)
    Abstract [en]

    Recent meta-analyses found an association between prenatal exposure to the antidepressant fluoxetine (FLX) and an increased risk of autism in children. This developmental disorder has been related to dysfunctions in the brains' rewards circuitry, which, in turn, has been linked to dysfunctions in dopaminergic (DA) signaling. The present study investigated if FLX affects processes involved in dopaminergic neuronal differentiation. Mouse neuronal precursors were differentiated into midbrain dopaminergic precursor cells (mDPCs) and concomitantly exposed to clinically relevant doses of FLX. Subsequently, dopaminergic precursors were evaluated for expression of differentiation and stemness markers using quantitative polymerase chain reaction. FLX treatment led to increases in early regional specification markers orthodenticle homeobox 2 (Otx2) and homeobox engrailed-1 and -2 (En1 and En2). On the other hand, two transcription factors essential for midbrain dopaminergic (mDA) neurogenesis, LIM homeobox transcription factor 1 alpha (Lmx1a) and paired-like homeodomain transcription factor 3 (Pitx3) were downregulated by FLX treatment. The stemness marker nestin (Nes) was increased, whereas the neuronal differentiation marker beta 3-tubulin (Tubb3) decreased. Additionally, we observed that FLX modulates the expression of several genes associated with autism spectrum disorder and downregulates the estrogen receptors (ERs) alpha and beta. Further investigations using ER beta knockout (BERKO) mDPCs showed that FLX had no or even opposite effects on several of the genes analyzed. These findings suggest that FLX affects differentiation of the dopaminergic system by increasing production of dopaminergic precursors, yet decreasing their maturation, partly via interference with the estrogen system.

  • 7.
    Norinder, Ulf
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Ahlberg, Ernst
    Carlsson, Lars
    Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 33-40Article in journal (Refereed)
    Abstract [en]

    Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source. The former models only result in valid predictions for the majority, non-mutagenic, class whereas the latter models are valid for both classes, i.e. mutagenic and non-mutagenic compounds. These results demonstrate the importance of data consistency manifested through the superior predictive quality and validity of the models based only on DGM/NIHS generated data compared to a combination of this data with public data sources.

  • 8.
    Norinder, Ulf
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Myatt, Glenn
    Ahlberg, Ernst
    Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction2018In: Biomolecules, E-ISSN 2218-273X, Vol. 8, no 3, article id 85Article in journal (Refereed)
    Abstract [en]

    The occurrence of mutagenicity in primary aromatic amines has been investigated using conformal prediction. The results of the investigation show that it is possible to develop mathematically proven valid models using conformal prediction and that the existence of uncertain classes of prediction, such as both (both classes assigned to a compound) and empty (no class assigned to a compound), provides the user with additional information on how to use, further develop, and possibly improve future models. The study also indicates that the use of different sets of fingerprints results in models, for which the ability to discriminate varies with respect to the set level of acceptable errors.

  • 9.
    Norinder, Ulf
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Naveja, J. J.
    Lopez-Lopez, E.
    Mucs, D.
    Medina-Franco, J. L.
    Conformal prediction of HDAC inhibitors2019In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 30, no 4, p. 265-277Article in journal (Refereed)
    Abstract [en]

    The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.

  • 10.
    Norinder, Ulf
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swedish Toxicology Sciences Research Center, Sweden.
    Rybacka, A.
    Andersson, P. L.
    Conformal prediction to define applicability domain - A case study on predicting ER and AR binding2016In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 27, no 4, p. 303-316Article in journal (Refereed)
    Abstract [en]

    A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.

  • 11.
    Norinder, Ulf
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Svensson, Fredrik
    Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 4, p. 1598-1604Article in journal (Refereed)
    Abstract [en]

    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.

  • 12. Svensson, Fredrik
    et al.
    Afzal, Avid M.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Swetox, Sweden.
    Bender, Andreas
    Maximizing gain in high-throughput screening using conformal prediction2018In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, article id 7Article in journal (Refereed)
    Abstract [en]

    Iterative screening has emerged as a promising approach to increase the efficiency of screening campaigns compared to traditional high throughput approaches. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models, resulting in more efficient screening. One way to evaluate screening is to consider the cost of screening compared to the gain associated with finding an active compound. In this work, we introduce a conformal predictor coupled with a gain-cost function with the aim to maximise gain in iterative screening. Using this setup we were able to show that by evaluating the predictions on the training data, very accurate predictions on what settings will produce the highest gain on the test data can be made. We evaluate the approach on 12 bioactivity datasets from PubChem training the models using 20% of the data. Depending on the settings of the gain-cost function, the settings generating the maximum gain were accurately identified in 8-10 out of the 12 datasets. Broadly, our approach can predict what strategy generates the highest gain based on the results of the cost-gain evaluation: to screen the compounds predicted to be active, to screen all the remaining data, or not to screen any additional compounds. When the algorithm indicates that the predicted active compounds should be screened, our approach also indicates what confidence level to apply in order to maximize gain. Hence, our approach facilitates decision-making and allocation of the resources where they deliver the most value by indicating in advance the likely outcome of a screening campaign.

  • 13. Svensson, Fredrik
    et al.
    Aniceto, Natalia
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swetox, Karolinska Institutet, Sweden.
    Cortes-Ciriano, Isidro
    Spjuth, Ola
    Carlsson, Lars
    Bender, Andreas
    Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty2018In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 5, p. 1132-1140Article in journal (Refereed)
    Abstract [en]

    Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

  • 14. Svensson, Fredrik
    et al.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Bender, Andreas
    Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction2017In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 57, no 3, p. 439-444Article in journal (Refereed)
    Abstract [en]

    High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a conformal predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than, when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the active compounds could be identified, compared to 13.5% when screening the same number of compounds solely based on docking. The use of conformal predictors also gives a clear indication of the number of compounds to screen in the next iteration. These results indicate that iterative screening based on molecular docking and conformal prediction can be an efficient way to find active compounds while screening only a small part of the compound collection.

  • 15. Svensson, Fredrik
    et al.
    Norinder, Ulf
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Swedish Toxicology Sciences Research Center, Sweden.
    Bender, Andreas
    Modelling compound cytotoxicity using conformal prediction and PubChem HTS data2017In: Toxicology Research, ISSN 2045-452X, Vol. 6, no 1, p. 73-80Article in journal (Refereed)
    Abstract [en]

    The assessment of compound cytotoxicity is an important part of the drug discovery process. Accurate predictions of cytotoxicity have the potential to expedite decision making and save considerable time and effort. In this work we apply class conditional conformal prediction to model the cytotoxicity of compounds based on 16 high throughput cytotoxicity assays from PubChem. The data span 16 cell lines and comprise more than 440 000 unique compounds. The data sets are heavily imbalanced with only 0.8% of the tested compounds being cytotoxic. We trained one classification model for each cell line and validated the performance with respect to validity and accuracy. The generated models deliver high quality predictions for both toxic and non-toxic compounds despite the imbalance between the two classes. On external data collected from the same assay provider as one of the investigated cell lines the model had a sensitivity of 74% and a specificity of 65% at the 80% confidence level among the compounds assigned to a single class. Compared to previous approaches for large scale cytotoxicity modelling, this represents a balanced performance in the prediction of the toxic and non-toxic classes. The conformal prediction framework also allows the modeller to control the error frequency of the predictions, allowing predictions of cytotoxicity outcomes with confidence.

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