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- W4386502261 endingPage "103763" @default.
- W4386502261 startingPage "103763" @default.
- W4386502261 abstract "Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active pharmaceutical ingredient and can lead to improvements in physicochemical properties of clinical relevance. At the same time, machine learning is finding its way into all areas of drug discovery and delivers impressive results. In this review, we attempt to provide an overview of machine learning, deep learning and network-based recommendation approaches applied to pharmaceutical co-crystallization. We also present crystal structure prediction as an alternative to machine learning approaches." @default.
- W4386502261 created "2023-09-08" @default.
- W4386502261 creator A5005689185 @default.
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- W4386502261 date "2023-11-01" @default.
- W4386502261 modified "2023-09-30" @default.
- W4386502261 title "In silico co-crystal design: assessment of the latest advances" @default.
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- W4386502261 doi "https://doi.org/10.1016/j.drudis.2023.103763" @default.
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