Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into the market-however, predicting the timescale involved with the onset of crystallization a priori is a formidably challenging task. Machine learning can help in this context by crafting models capable of predicting the physical stability of any given amorphous drug.
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