Abstract
A random forest model has for the first time enabled the prediction of the crystallisability (crystals vs. no crystals) of organic molecules with ∼70% accuracy. The predictive model is based on calculated molecular descriptors and published experimental crystallisation propensities of a library of substituted acylanilides.
| Original language | English |
|---|---|
| Pages (from-to) | 4272-4275 |
| Number of pages | 4 |
| Journal | CrystEngComm |
| Volume | 17 |
| Issue number | 23 |
| Early online date | 16 Feb 2015 |
| DOIs | |
| Publication status | Published - 21 Jun 2015 |
Keywords
- crystallisability
- acylanilides
- molecular descriptors
- Machine Learning
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