TY - JOUR T1 - Design of a bioinformatics model to predict drug compound properties and its application in inhibition of HIV replication and BACE-1 TT - طراحی یک مدل بیوانفورماتیکی برای پیش‌‌بینی فعالیت ترکیبات دارویی و کاربرد آن بر مهار تکثیر HIV و ژن BACE-1 JF - gebsj JO - gebsj VL - 9 IS - 2 UR - http://gebsj.ir/article-1-363-en.html Y1 - 2020 SP - 181 EP - 193 KW - Edge weighted Graph Convolutional Network KW - Molecular bonds KW - Deep learning KW - Transfer learning. N2 - In this paper, a new method for the problem of predicting the compound molecule properties in the lead optimization step in drug design is presented. In the lead optimization step, the amount of available biological data on small molecule compounds is low. In recent years, this challenge has been considered and transfer learning and deep learning techniques have been used to solve it. For this purpose, similar data sets have been used as auxiliary data to learn a reliable model. In this method, compound feature extraction plays an essential role in transferring knowledge from similar (auxiliary) data sets to the target data set. In this paper, the effect of using Edge weighted Graph Convolutional Network (EGCN) is assessed which able to consider the feature vector of the compound bond as well as the atom feature vector. To evaluate the method, we have applied the proposed approach on BACE and HIV datasets. The obtained results show that the proposed method is able to extract more efficient knowledge from similar data sets to transfer to the target data set. M3 ER -