Message passing neural networks for molecular property prediction

Developing new drugs relies heavily on understanding the various molecular properties of potential drug candidates. While experimental assays performed in the lab are the best source of information about molecular properties, these assays are slow and expensive. For this reason, there has been great interest in the potential of machine learning models to predict molecular properties without the need for experimental assays. However, recent literature has not yet clearly determined which machine learning models are optimal for molecular property prediction. In this thesis, I apply the Direct Message Passing Neural Network (D-MPNN) from [47, 48] to 19 publicly available property prediction datasets, and I demonstrate that it consistently outperforms prior machine learning models. Additionally, I introduce several optimizations to the D-MPNN which further enhance its performance and lead to new state-of-the-art results.

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

• Swanson, Kyle(Kyle W.)

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/1721.1/123133

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123133