Message passing neural networks for molecular property prediction
Name
1128814048-MIT.pdf
Size
5.86 MB
Format
Adobe PDF
Checksum (MD5)
d90156c66c6b00bd5ca919e12bffe72d
Author(s)
Swanson, Kyle(Kyle W.)
Advisor(s)
Regina Barzilay.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-84).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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