Generating rationale for molecular prediction using reinforcement learning
Name
1102049756-MIT.pdf
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
596c6897ee1ffd8ca4f0bc5aebb94316
Author(s)
Chen, Benson(Benson S.)
Advisor(s)
Regina Barzilay.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
This thesis studies generation of rationale for neural prediction problems using reinforcement learning. In particular, we focus on neural predictions in chemical property prediction tasks. We design a reinforcement learning agent that learns to incrementally extract the important regions of molecular graphs, and construct a predictor trained on only the selected regions. The ability for the model to predict a property based only on the partial graph exemplifies the importance of these substructures and therefore can be interpreted as rationales for the prediction task. We test our reinforcement learning model on several chemical datasets and show that our model can generate meaningful rationales while maintaining good predictive performances.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-45).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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