Utilization of new approaches in the determination of drug solubility in supercritical fluids can reduce the computation time and represent reliable results. This also leads to more applications of the supercritical technology in the field of drug manufacturing. A least-square support vector machine (LSSVM) approach is employed in this study in order to predict 33 different drug solubility in supercritical CO₂. The solubility of the drugs is estimated as a function of temperature, pressure, supercritical CO₂ density, and 20 different chemical substructures. LSSVM results are then compared to those obtained from 8 previously reported semi-empirical correlations. Satisfying predictions are performed by the proposed LSSVM with an average absolute relative deviation of 4.92% and determination coefficient of 0.998 for the testing dataset. Therefore, the proposed LSSVM can be applied as a reliable predictive tool to estimate the drugs’ solubility, if drugs’ chemical structures are given.

利用新方法测定超临界流体中的药物溶解度可以减少计算时间并代表可靠的结果。这也导致超临界技术在药物制造领域的更多应用。为了预测33种不同的药物在超临界CO _2中的溶解度，本研究采用最小二乘支持向量机（LSSVM）方法。估计药物的溶解度是温度，压力，超临界CO ₂的函数密度和20种不同的化学亚结构。然后将LSSVM结果与从8个先前报告的半经验相关中获得的结果进行比较。拟议的LSSVM可以对测试数据集进行满意的预测，平均绝对相对偏差为4.92％，确定系数为0.998。因此，如果给出了药物的化学结构，则所提出的LSSVM可以用作估计药物溶解度的可靠预测工具。