Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Deep Transferable Compound Representation Across Domains and Tasks for Low Data Drug Discovery Publisher Pubmed



Abbasi K1 ; Poso A2 ; Ghasemi J3 ; Amanlou M4 ; Masoudinejad A1
Authors

Source: Journal of Chemical Information and Modeling Published:2019


Abstract

The main problem of small molecule-based drug discovery is to find a candidate molecule with increased pharmacological activity, proper ADME, and low toxicity. Recently, machine learning has driven a significant contribution to drug discovery. However, many machine learning methods, such as deep learning-based approaches, require a large amount of training data to form accurate predictions for unseen data. In lead optimization step, the amount of available biological data on small molecule compounds is low, which makes it a challenging problem to apply machine learning methods. The main goal of this study is to design a new approach to handle these situations. To this end, source assay (auxiliary assay) knowledge is utilized to learn a better model to predict the property of new compounds in the target assay. Up to now, the current approaches did not consider that source and target assays are adapted to different target groups with different compounds distribution. In this paper, we propose a new architecture by utilizing graph convolutional network and adversarial domain adaptation network to tackle this issue. To evaluate the proposed approach, we applied it to Tox21, ToxCast, SIDER, HIV, and BACE collections. The results showed the effectiveness of the proposed approach in transferring the related knowledge from source to target data set. © 2019 American Chemical Society.
Other Related Docs
5. The Role of Artificial Intelligence in Pharmaceutical Sciences, Journal of Isfahan Medical School (2025)
8. Deep Neural Network in Qsar Studies Using Deep Belief Network, Applied Soft Computing Journal (2018)