Automated Synthetic Feasibility Assessment: A Data-driven Derivation of Computational tools for Medicinal Chemistry
Year: 2014
Type: dissertation
Abstract: The planning of organic syntheses, a critical problem in chemistry, can be directly modeled as resource- constrained branching plans in a discrete, fully-observable state space. Despite this clear relationship, the full artillery of artificial intelligence has not been brought to bear on this problem due to its inherent complexity and multidisciplinary challenges. In this thesis, I describe a mapping between organic synthesis and heuristic search and build a planner that can solve such problems automatically at the undergraduate level. Along the way, I show the need for powerful heuristic search algorithms and build large databases of synthetic information, which I use to derive a qualitatively new kind of heuristic guidance.
Source: TSpace (University of Toronto)
Author: Abraham Heifets
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2563423759