Improving Compound Activity Classification via Deep Transfer and Representation Learning

by   Vishal Dey, et al.

Recent advances in molecular machine learning, especially deep neural networks such as Graph Neural Networks (GNNs) for predicting structure activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks are limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer learning such as pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to leverage source domain data and transfer useful information to the target domain. TAc learns to generate effective molecular features that can generalize well from one domain to another, and increase the classification performance in the target domain. Additionally, TAc-fc extends TAc by incorporating novel components to selectively learn feature-wise and compound-wise transferability. We used the bioassay screening data from PubChem, and identified 120 pairs of bioassays such that the active compounds in each pair are more similar to each other compared to its inactive compounds. Overall, TAc achieves the best performance with average ROC-AUC of 0.801; it significantly improves ROC-AUC of 83 compared to the best baseline FCN-dmpna (DT). Our experiments clearly demonstrate that TAc achieves significant improvement over all baselines across a large number of target tasks. Furthermore, although TAc-fc achieves slightly worse ROC-AUC on average compared to TAc (0.798 vs 0.801), TAc-fc still achieves the best performance on more tasks in terms of PR-AUC and F1 compared to other methods.


page 36

page 39


Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs

Despite the remarkable success achieved by graph convolutional networks ...

Transfer Value Iteration Networks

Value iteration networks (VINs) have been demonstrated to be effective i...

Low Data Drug Discovery with One-shot Learning

Recent advances in machine learning have made significant contributions ...

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning

Graph neural networks have become very popular for machine learning on m...

Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets

Graphs provide a powerful means for representing complex interactions be...

Reprogramming Language Models for Molecular Representation Learning

Recent advancements in transfer learning have made it a promising approa...

Tackling Imbalanced Data in Cybersecurity with Transfer Learning: A Case with ROP Payload Detection

In recent years, deep learning gained proliferating popularity in the cy...

Please sign up or login with your details

Forgot password? Click here to reset