Chemical & Pharmaceutical Bulletin ( IF 1.7 ) Pub Date : 2021-10-01 , DOI: 10.1248/cpb.c20-00866 Guoliang Bai 1 , Tiantian Wu 2 , Libo Zhao 1 , Xiaoling Wang 1 , Shan Li 2 , Xin Ni 1
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with bitter medicine. Therefore, judging whether a drug is bitter and adopting flavor correction and taste-masking strategies are key to solving the problem of drug compliance in children. Although various machine learning models for bitterness and sweetness prediction have been reported in the literature, no learning model or bitterness database for children’s medication has yet been reported. In this study, we trained four different machine learning models to predict bitterness. The goal of this study was to develop and validate a machine learning model called the “Children’s Bitter Drug Prediction System” (CBDPS) based on Tkinter, which predicts the bitterness of a medicine based on its chemical structure. Users can enter the Simplified Molecular-Input Line-Entry System (SMILES) formula for a single compound or multiple compounds, and CBDPS will predict the bitterness of children’s medicines made from those XGBoost–Molecular ACCess System (XgBoost–MACCS) model yielded an accuracy of 88% under cross-validation.
Fullsize Image中文翻译:
CBDPS 1.0:用于机器学习模型的 Python GUI 应用程序,用于预测儿童的苦味口服药物
苦味天生令人厌恶,被认为有助于保护动物免于摄入毒药。儿童对药味极为敏感,对苦药的依从性尤其差。因此,判断药物是否苦,采取矫味和掩味策略是解决儿童用药依从性问题的关键。尽管文献中已经报道了各种用于苦味和甜味预测的机器学习模型,但尚未报告儿童用药的学习模型或苦味数据库。在这项研究中,我们训练了四种不同的机器学习模型来预测苦味。本研究的目标是开发和验证一种基于 Tkinter 的机器学习模型,称为“儿童苦味药物预测系统”(CBDPS),它根据药物的化学结构预测药物的苦味。用户可以输入单一化合物或多种化合物的简化分子输入线路输入系统 (SMILES) 公式,CBDPS 将预测由这些 XGBoost–分子访问系统 (XgBoost–MACCS) 模型制成的儿童药物的苦味,产生了准确性88% 的交叉验证。
全尺寸图像