Amorphous solid dispersion (SD) is an effective solubilization technique for water-insoluble drugs. However, physical stability issue of solid dispersions still heavily hindered the development of this technique. Traditional stability experiments need to be tested at least three to six months, which is time-consuming and unpredictable. In this research, a novel prediction model for physical stability of solid dispersion formulations was developed by machine learning techniques. 646 stability data points were collected and described by over 20 molecular descriptors. All data was classified into the training set (60%), validation set (20%), and testing set (20%) by the improved maximum dissimilarity algorithm (MD-FIS). Eight machine learning approaches were compared and random forest (RF) model achieved the best prediction accuracy.

---

The drug-phospholipid complexation technique is widely used as an increasingly common method to improve the bioavailability of drugs. This project aims to predict the complexation rate of phospholipid complex by machine learning methods. 363 drug/phospholipid complex formulations were collected from the literature. The datasets were trained by several popular machine learning methods including RandomForest, SVM, KNN, Naive Bayes, Decision Tree, XGBoost, LightGBM. By analysis and compare these methods, the RandomForest got better performance. The predictive accuracy and AUC of RandomForest was 0.772/0.824 on training set(5-fold cross validation), 0.808/0.902 for the test set, respectively.

---

Nanocrystals have exhibited great advantages for solving the dissolution issue of water insoluble drugs. In this study, the machine learning models for the three nanocrystal preparation methods were constructed for the nanocrystal size and PDI predictions. The results demonstrated that random forest (RF) exhibited well predictive performance for the ball wet milling (BWM), high-pressure homogenization (HPH) methods and anti solvent method (ASP). Here, by submitting a molecule, we can predict both size and PDI for the three methods.

---

Self-emulsifying drug delivery system (SEDDS), a thermodynamic stable nano formulation, is consists of oil, surfactant, co-surfactant and APIs. For oral administration, drugs are dissolved in the SEDDS instead of solid state, which benefit to the absorption in the Gi tract. Here, a pseudo-ternary phase diagram predicting model was constructed by random forest algorithm and this model could predict the self-emulsion status of the formulation under specified conditions.

---

Liposome is a spherical vesicle that consists of a bilayer of lipid and internal hydrophilic cavity with particle sizes ranging from 30 nm to several micrometers. As its great biocompatibility, biodegradability and low toxicity, liposome is regarded as a promising drug delivery system, not only for drug molecule delivery, but for genes and bio-macromolecules. The composition of lipids, particle size, surface charge and lamellarity would decide the structure and morphology of liposome particles. The various preparation methods have been applied in liposome products. With the development of liposome technology, a number of liposome-based drug formulations have been available for market and for clinic trails. In current research, size, PDI, zeta potential and encapsulation of liposome are regarded as key parameters for formulation evaluation. Herein, we develop a prediction platform for these key parameters of liposome.

---

Drug solubility is a basic property for the formulation design, especially in different solvents. Here, we collected a current largest dataset containing more than 4000 data points. Then, we employed lightGBM and RandomForest algorithms to build a robust model. This tool enables users to predict drug solubility in 27 kinds of solvent.

---