当前位置: X-MOL 学术J. Chem. Inf. Model. › 论文详情
GRAM: A True Null Model for Relative Binding Affinity Predictions
Journal of Chemical Information and Modeling ( IF 3.966 ) Pub Date : 2020-01-02 , DOI: 10.1021/acs.jcim.9b00939
Guanglei Cui; Alan P. Graves; Eric S. Manas

Relative binding affinity prediction is a critical component in computer aided drug design. A significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of being random in an objective manner. Although many performance metrics, such as the Pearson correlation coefficient (r), mean unsigned error (MUE), and root-mean-square error (RMSE), are frequently used in the literature, a true and nontrivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely, the prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are (1) it provides the uncertainty range in the predicted activities, which is important in prospective applications, and (2) a true null model with well-defined PI can be established. We provide one such example termed the Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, σ). Having an analytically defined PI that only depends on the variation in the activities, GRAM should, in principle, allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.
更新日期:2020-01-02

 

全部期刊列表>>
化学/材料学中国作者研究精选
Springer Nature 2019高下载量文章和章节
《科学报告》最新环境科学研究
ACS材料视界
自然科研论文编辑服务
中南大学国家杰青杨华明
剑桥大学-
中国科学院大学化学科学学院
材料化学和生物传感方向博士后招聘
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug