SCRAMBLE’N’GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models

Lipiński, Piotr F. J.; Szurmak, Przemyslaw

doi:10.1007/s11696-017-0215-7

Cited by 27 publications

(21 citation statements)

References 58 publications

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“…Secondly, all models achieved higher prediction accuracy than y-scrambling models (Fig. 1 ), demonstrating they all had a predictive power exceeding that of pure chance [ 30 ]. Thirdly, for LOCO-CV and external test set a slightly better predictive performance was found using the highest confidence dataset in comparison to the lower confidence datasets, although it should be noted that these models are not directly comparable given the varying dataset sizes (Fig.…”

Section: Resultsmentioning

confidence: 99%

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

et al. 2021

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Background Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models. Results As part of the Critical Assessment of Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 (http://camda2019.bioinf.jku.at), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose. Conclusion Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

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Section: Resultsmentioning

confidence: 99%

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

et al. 2021

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“…demonstrating they all had a predictive power exceeding that of pure chance (28). Thirdly, for LOCO-CV and external test set a slightly better predictive performance was found using the highest confidence dataset in comparison to the lower confidence datasets (Fig.…”

Section: Predictive Modelingmentioning

confidence: 79%

Prediction and mechanistic analysis of Drug-Induced Liver Injury (DILI) based on chemical structure

Liu

Walter

Wright

et al. 2020

Preprint

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Background: Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.Results: As part of the Critical Assessment of Massive Data Analysis (CAMDA) “CMap Drug Safety Challenge” 2019 (http://papers.camda.info/), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold cross-validation inside a 10-fold training scheme of 0.759±0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, and the previously established stress response pathways mediated by mitochondria, p38 MAPK and TP53. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DDR2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.Conclusion: Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

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“…This is a form of arrangement test, where the values of the dependent variable ( y ) are randomly assigned to different compounds, whereas the descriptor values ( x ’s) are left unchanged. 29 The rearranged data are then used for training QSAR models. As shown in Table 2 , the value of λ (Wilks) increases significantly in all the validation models.…”

Section: Resultsmentioning

confidence: 99%

Molecular Topology QSAR Strategy for Crop Protection: New Natural Fungicides with Chitin Inhibitory Activity

et al. 2020

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Nowadays, crop protection is a major concern and how to proceed is a delicate point of contention. New products must be safe and ecofriendly in accordance with the actual legislation. In this context, we present a quantitative structure–activity relationship strategy based on molecular topology as a tool for generating natural products as potential fungicides following a mechanism of action based on the synthesis of chitin inhibition (chitinase inhibition). Two discriminant equations using statistical linear discriminant analysis were used to identify three potential candidates (1-methylxanthine, hematommic acid, and antheraxanthin). The equations showed accuracy and specificity levels above 80%, minimizing the risk of selecting false active compounds.

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SCRAMBLE’N’GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models

Cited by 27 publications

References 58 publications

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Prediction and mechanistic analysis of Drug-Induced Liver Injury (DILI) based on chemical structure

Molecular Topology QSAR Strategy for Crop Protection: New Natural Fungicides with Chitin Inhibitory Activity

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