An adaptive model for learning molecular endpoints

Similarity-based learning on structures

计算机科学;物理学

Ian Walsh1 ; Alessandro Vullo1 ; Gianluca Pollastri1

PID  :  80216

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

Accurately predicting the endpoints of chemical compounds is a important step towards drug design and molecular screening in particular. Here we develop a recursive architecture that is capable of mapping Undirected Graphs into individual labels, and apply it to the prediction of a number of dif ferent properties of small molecules. The results we obtain are generally stateof theart. The final model is completely general and may be applied not only to prediction of molecular properties, but to a vast range of problems in which the input is a graph and the output is either a single property or (with small modifications) a set of properties of the nodes.