2023
DOI: 10.1021/acs.chemrestox.3c00086
|View full text |Cite
|
Sign up to set email alerts
|

Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data

Abstract: Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that disrupt the function of these organelles can lead to serious side effects, including liver injury and cardiotoxicity. Different in vitro assays exist to detect mitochondrial toxicity at varying mechanistic levels: disruption of the respiratory chain, disruption of the membrane potential, or general mitochondrial dysfunction. In parallel, whole cell imaging assays like Cell Painting provide a phenotypic overview of … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1
1

Relationship

1
6

Authors

Journals

citations
Cited by 10 publications
(9 citation statements)
references
References 41 publications
0
9
0
Order By: Relevance
“…Specifically, clustering compounds or genes by morphological similarity provides mechanistic insights from annotated cluster members. Alternatively, classifiers trained on extracted features can predict downstream tasks, such as drug toxicity 24 or cell health phenotypes 25 . However, label scarcity generally precludes end-to-end supervised learning from images, with only few exceptions 26 .…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, clustering compounds or genes by morphological similarity provides mechanistic insights from annotated cluster members. Alternatively, classifiers trained on extracted features can predict downstream tasks, such as drug toxicity 24 or cell health phenotypes 25 . However, label scarcity generally precludes end-to-end supervised learning from images, with only few exceptions 26 .…”
Section: Introductionmentioning
confidence: 99%
“…The methods described in this special issue cover a wide range of AI methods ranging from expert systems, ,, over similarity measures including read-across methods, to classical machine learning such as random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) ,, to deep learning (DL) methods ,, , including equivariant neural networks, deep generative models, and even large language models . In addition to models relying purely on the chemical structure, there is a notable trend of bringing in additional modalities to improve or inform predictive models. , In the following, we provide an overview of the AI approaches used in the publications contained in the SI. The methodological overview presented in this section highlights the variety of AI approaches employed in toxicology, showcasing the potential for combining different methods to enhance predictive performance and expand the scope of toxicity modeling.…”
Section: Methodological Overviewmentioning
confidence: 99%
“…Most of the presented methods perform prediction tasks on molecules (for exceptions, see paragraphs below) and, thus, have to use representations of molecules. While for data handling, the simplified molecular-input line-entry system (SMILES) representation is often used, the predominant representations of molecules for modeling are still extended connectivity fingerprint (ECFP) or Morgan fingerprints. , Several publications use graph neural networks that operate on the molecular graph. , Aside from the chemical structure, there is a growing tendency to incorporate biological characterizations and read-outs, for example, via cell morphology , or transcriptomics . The utilization of diverse representations, ranging from molecular structures to biological features, enhances the predictive models showcased in this section and could improve the comprehensive understanding of toxicological properties.…”
Section: Overview Of Used Representationsmentioning
confidence: 99%
See 1 more Smart Citation
“…Cell painting is a morphological profiling method in which cells are perturbed with a compound, have their different compartments stained using six dyes, and have their images of the five fluorescence channels captured. 4 Cell painting data have been used for a range of applications, from predicting mitochondrial toxicity, 3,11 in vitro toxicity, 12 hit identification, 13 and more. In addition, cell painting can be used in combination with other modalities to enhance prediction such as chemical structure 14 and gene expression data.…”
Section: Introductionmentioning
confidence: 99%