关键词: 热力学模型, 机器学习, 量化计算, 分子模拟, 自组装纳米粒

Abstract:

Carrier-free self-assembled nanomedicine has attracted wide attention and become one of the powerful strategies for cancer treatment due to its unique advantages such as simple preparation process, high drug loading, low cost and avoiding the toxicity caused by carriers. However, with millions of possible drug combinations, determining whether they can form self-assembling nanoparticles is a major challenge. With indomethacin as the model drug, different types of anti-tumor drugs were selected to explore their ability to form self-assembled nanoparticles with indomethacin, and different methods such as Hansen solubility parameters, machine learning model, binding energy calculation and COSMO-RS theory were utilized to study the self-assembly behavior. The self-assembly process was visualized by molecular dynamics simulation and quantitative chemistry calculation was used to analyze the molecular interaction to reveal the driving force of the self-assembly process. It was found that the machine learning model based on the training of high-throughput experimental values can quickly predict the probability of self-aggregation and co-aggregation with indomethacin molecules, and the combination of drug molecules can be preliminarily screened. In addition, through the study of thermodynamic mechanism, suitable drug molecule combinations can be selected from the perspective of energy and charge distribution, including the comparison of binding energy between the drug molecule itself and two different drug molecules, Hansen solubility parameter difference and surface charge density distribution. The self-assembly behavior is predicted using the Hansen solubility parameter model, COSMO-RS theory, and binding energy acquisition descriptors, and compared with the prediction of the machine learning model based on molecular fingerprints as descriptors. Based on molecular dynamics simulation, it was found that the self-assembly of drug molecules to form nanoparticles is a spontaneous aggregation behavior. Further analysis of the weak interaction between molecules revealed that hydrogen bond interaction is the key factor driving the self-assembly of drug molecules. Based on the research results of this paper, the different eigenvalues of drug molecules calculated by the thermodynamic model can be coupled into the machine learning model to enhance the physical meaning of the machine learning model, and an intelligent screening platform for self-assembled nanomedicine can be established, providing important guidance for the design and preparation of carrier-free nanomedicine delivery system with high delivery efficiency and combination therapy.

Key words: thermodynamic model, machine learning, quantum chemistry calculation, molecular simulation, self-assembling nanoparticles