2023
DOI: 10.1007/978-3-031-20730-3_12
|View full text |Cite
|
Sign up to set email alerts
|

Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 103 publications
0
0
0
Order By: Relevance
“…This approach significantly reduces the need for time-consuming and costly experimental assays. Central to QSAR/QSPR analysis is the concept of molecular similarity, which is usually measured based on various molecular descriptors and fingerprints [7,8]. Molecular descriptors are numerical descriptions of the structural features of a chemical and are widely used in the development of predictive models of predicting biological activity and chemical properties [9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…This approach significantly reduces the need for time-consuming and costly experimental assays. Central to QSAR/QSPR analysis is the concept of molecular similarity, which is usually measured based on various molecular descriptors and fingerprints [7,8]. Molecular descriptors are numerical descriptions of the structural features of a chemical and are widely used in the development of predictive models of predicting biological activity and chemical properties [9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%