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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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S.NakagawaT.TsubouchiH.IdoA.InaokaT.OnoK.et al