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Theoretical approaches are formulated to investigate the molecular mobilityunder various cooling rates of amorphous drugs. We describe the structuralrelaxation of a tagged molecule as a coupled process of cage-scale dynamics andcollective molecular rearrangement beyond the first coordination shell. Thecoupling between local and non-local dynamics behaves distinctly in differentsubstances. Theoretical calculations for the structural relaxation time, glasstransition temperature, and dynamic fragility are carried out over twenty-twoamorphous drugs and polymers. Numerical results have a quantitatively goodaccordance with experimental data and the extracted physical quantities usingthe Vogel-Fulcher-Tammann fit function and machine learning. The machinelearning method reveals the linear relation between the glass transitiontemperature and the melting point, which is a key factor for pharmaceuticalsolubility. Our predictive approaches are reliable tools for developing drugformulation.

rsc advances

Effects of Cooling Rate on Structural Relaxation in Amorphous Drugs:  Elastically Collective Nonlinear Langevin Equation Theory and Machine  Learning Study