Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Abstract Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-08-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-021-00539-7 |