Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
Broad Institute · University of Cambridge · +19 more institutions
Abstract
Translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction. For instance, models can be used to select promising compounds for on-target effects or to deselect those with undesirable characteristics (e.g., off-target or ineffective due to unfavorable pharmacokinetics). However, reliance on ML is not without risks, due to biases stemming from nonrepresentative training data, incompatible choice of algorithm to represent the underlying data, or poor model building and validation approaches. This might lead…
Citation impact
- FWCI
- 96.41
- Percentile
- 100%
- References
- 382
Authors
33Topics & keywords
- Chemical toxicity
- Toxicity
- Machine learning
- Artificial intelligence
- Computer science
- Chemistry
- Organic chemistry
- Zero hunger