Machine learning of phase diagram based on GPR for the DPD simulation of drug delivery to endothelial cells

Despite huge effort over the years, the design of functionalized nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative Particle Dynamics (DPD) simulations, combined with an energy calculation method are used to find the phase diagram for adhesion of the designed NCs to the endothelial cell under the influence of series of parameters such as the shape, size, and ligand density of the NCs. However, preparing a phase diagram requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by applying a Gaussian Process Regression (GPR)-informed active learning strategy to the DPD results to drastically reduce the number of necessary simulations. We then validate the use of the ML-informed methodology by investigating computationally the morphology, dynamics, and inclusion free energy of NCs.