Systematic Evaluation of logD7.4 based on Ensemble Machine Learning,Group Contribution and Matched M

• 作 者: Li Fu Dong-Sheng Cao
• 机 构: Xiangya School of Pharmaceutical Sciences,Central South University,Changsha,410013
• 出 处: 2019中国化学会第十五届全国计算(机)化学学术会议
• 发表日期: 2019年3期
• 关键词: logD7.4 XGBoost Group Contribution MMP Ensemble Learning

　　Lipophilicity,as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4(logD7.4),is a major determinant of various absorption,distribution,metabolism,elimination and toxicology(ADMET) parameters of drug candidates.In this study,we developed several quantitative structure-property relationship(QSPR) models to predict logD7.4 based on a large and structurally diverse data set.