Generating rationale for molecular prediction using reinforcement learning

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Chen, Benson(Benson S.)

Advisor dc:contributor.advisor

• Regina Barzilay.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121735

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121735