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Methods such as machine-learning regression models (such as random forests 259,260,261,262 or support vector regression 263 ) and deep learning techniques (such as neural networks 255,264,265 and graph neural networks 266,267,268 for molecular features) can be used to understand complicated, nonlinear connections between formulation factors — such as particle size, surface charge, hydrophobicity and excipient makeup — and their pharmacokinetic or pharmacodynamic profiles 269,270

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