Ternary cyclodextrin (CD) complexes (drug/CD/polymer) can effectively improve the solubility of water-insoluble drugs with large size than binary CD formulations. However, ternary formulations are screened by a trial-and-error approach, which is laborious and material-wasting. Current research aims to develop a prediction model for ternary CD formulations by combined machine learning and molecular modeling.596 ternary formulations data were collected to build a prediction model by machine learning. The random forest model achieved good performance with R²=0.882 in ST prediction and R²=0.822 in ST/SB prediction. Two ternary formulations(Hydrocortisone/β-CD/HPMC and dovitinib/γ-CD/CMC) were used to validate the prediction model. Molecular modeling results showed that HPMC not only warped around hydrocortisone but also prevented CD molecules from self-aggregation to increase solubility. In conclusion, a prediction model for the ternary CD formulations was successfully developed, which will significantly accelerate the formulation screening process to benefit the formulation development of water-insoluble drugs. Keywords: ternary cyclodextrin complexes, solubility prediction, machine learning. molecular modeling, random forest

與二元環糊精(藥物/環糊精)製劑相比，三元環糊精合物(藥物/環糊精/聚合物)能有效改善水難溶性藥物的溶解度。目前，三元製劑的配方選是通過實驗試錯的方法進行的，這既費時又費力。此研究旨在通過結合機器學習和分子模擬技術，建立三元環糊精製劑的預测模型。我們收集了 596 個三元製劑配方的數據，通過機器學習算法建立預测模型。其中隨機森林模型取得了良好的性能，在預測三元環糊精複合物溶解度任務上，模型 R²=0.882，在預测三元環糊精比二元環糊精溶解度提高倍數任務上，R²=0.822。随後，我們選用了兩個不在數據集中的三元複合物製劑(氫化可的松/β-環糊精/HPMC 和多維尼布/γ-環糊精/CMC)用來驗證所建立的預測模型，預測結果與實驗結果相近。最後，我們用分子模擬技術探究了氢化可的松/β-環糊精/HPMC 複合物的增溶機理。結果顯示，HPMC 不僅通過包裹氫化可的松來提高藥物的水溶性，而且還能阻止環糊精分子的自聚集以增加溶解度。總而言之，我們成功建立了三元環糊精製劑的預测模型，這将大大加快環糊精製劑配方篩選過程，有利於水難溶性物的製劑開發。 關鍵詞:三元環糊精合物，溶解度预测，機器學習，分子模擬，随機森林