2026-09-19T21:54:17Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1217352021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Chen, Benson(Benson S.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-17T20:59:09Z
2019-07-17T20:59:09Z
2019
2019
https://hdl.handle.net/1721.1/121735
1102049756
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).
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.
by Benson Chen.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
45 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Generating rationale for molecular prediction using reinforcement learning
Thesis
application/pdf
2019-07-17T20:59:06Z
Publication
Master
EECS
unknown
unknown
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