Computer assistance in organic synthesis planning and execution

The identification and synthesis of molecules that exhibit a desired function is an essential part of addressing contemporary problems in science and technology. Small molecules are the predominant solution to challenges in the development of medicines, chemical probes, specialty polymers, and organocatalysts, among others. The typical discovery paradigm is an iterative process of designing candidate compounds, synthesizing those compounds, and testing their performance. The rate at which this process yields successful compounds can be limited by bottlenecks and mispredictions at all three stages and is plagued by inefficiencies, not the least of which is the manual nature of synthesis planning and execution. This thesis describes techniques to streamline the synthesis of small molecules in this context of pharmaceutical discovery from two perspectives: one experimental and the other using techniques in data science and machine learning.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Coley, Connor Wilson.

Advisor dc:contributor.advisor

• Klavs F. Jensen and William H. Green.

• Chemical Engineering.

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/1721.1/122903

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122903