A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Belfield, Samuel J.; Firman, James W.; Enoch, Steven J.; Madden, Judith C.; Tollefsen, Knut Erik; Cronin, Mark

doi:10.1016/j.comtox.2022.100251

Cited by 14 publications

(11 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another possible application is the modeling of properties of molecular mixtures. Unlike existing single-instance approaches, 155,156 a mixture can be considered as a bag of individual components representing instances. Establishing relationships between individual components and key instance detection by dedicated MIL algorithms would represent an additional benefit.…”

Section: Perspectivesmentioning

confidence: 99%

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

show abstract

Section: Perspectivesmentioning

confidence: 99%

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

View full text Add to dashboard Cite

show abstract

“…Besides the issue of data leakage, predictive ecotoxicology lacks commonly recognized best practices such as benchmark datasets and reporting standards 14,[17][18][19][20] . As a part of ML-based research, it faces a reproducibility crisis, partly caused by inconsistent and in-transparent reporting (including underlying computer code), which prevents peer-reviewers from adequately assessing the findings, the modeling, and the data those findings are based on.…”

Section: Introductionmentioning

confidence: 99%

“…Several efforts aim to sensitize researchers to common pitfalls 19,20 and to motivate them to adopt checklist-based reporting standards, such as REFORMS proposed by Kapoor et al (2023) 21 . For QSAR models, similar quality standards have already been proposed (with 49 assessment criteria covering various aspects of QSAR development, documentation and use) 17 and further developed specifically for the application of ML methods to QSARs 18 . Furthermore, the FAIR (Findable, Accessible, Interoperable, Reusable) principles, which were developed for data sharing, could be adapted to model description and deployment and therefore help to improve the reproducibility and largescale adoption of these methods, and eventually turn them into a (re)usable resource for chemical safety assessment 6 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

Gasser,

Schür,

Perez-Cruz

et al. 2024

Preprint

View full text Add to dashboard Cite

Regulation of chemicals requires knowledge of their toxicological effects on a large number of target species. Traditionally, this knowledge has been acquired throughin vivotesting. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schüret al.,Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on how a chemical is represented, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to predict the toxicity of single chemicals accurately enough. Predictions for single chemicals are dominated by a few chemical properties and taxonomic traits are not captured sufficiently by the models. As a consequence, the models are not yet applicable in the regulatory process. Nevertheless, this work contributes to the ongoing discussions on how machine learning can be integrated in the regulatory process.Environmental significanceConventional environmental hazard assessment in its current form will not be able to adapt to the growing need for toxicity testing. Alternative methods, such as toxicity prediction through machine learning, could fulfill that need in an economically and ethically sound manner. Proper implementation, documentation, and the integration into the regulatory process are prerequisites for the usability and acceptance of these models.

show abstract

“…While in silico toxicology (IST) offers benefits in terms of cost-effectiveness, high throughput, and ethical considerations, its ability to predict complex biological end points is still under debate . Another difficulty is its integration with experimental data for risk assessment purposes, particularly in regulatory setups. − …”

Section: Introductionmentioning

confidence: 99%

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Rodríguez-Belenguer,

Mangas-Sanjuan,

Soria-Olivas

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure−Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

show abstract

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Cited by 14 publications

References 79 publications

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Contact Info

Product

Resources

About