Abstract
AbstractDevelopments and accessibility of computational methods within machine learning and deep learning have led to the resurgence of methods for computer assisted synthesis planning (CASP). In this paper we introduce our viewpoints on the analysis of reaction data, model building and evaluation. We show how the models’ performance is affected by the specificity of the extracted reaction rules (templates) and outline the direction of research within our group.
Highlights
IntroductionWith the increasing availability of reaction data, developments and accessibility of computational methods, and a drive to further automate design, make, test, analyze (DMTA) cycles within drug discovery [1], computer assisted synthesis planning (CASP) has seen renewed interest as of late [2]
With the increasing availability of reaction data, developments and accessibility of computational methods, and a drive to further automate design, make, test, analyze (DMTA) cycles within drug discovery [1], computer assisted synthesis planning (CASP) has seen renewed interest as of late [2]. This has been spurred by recent achievements in the application of neural networks combined with search algorithms [3, 4], learning from breakthroughs in their application to games such as chess and Go [5]
Recent studies have shown that neural network policies framed as multi-class classification problems can identify likely reactions through the noisy knowledge base [3, 4]
Summary
With the increasing availability of reaction data, developments and accessibility of computational methods, and a drive to further automate design, make, test, analyze (DMTA) cycles within drug discovery [1], computer assisted synthesis planning (CASP) has seen renewed interest as of late [2]. This has been spurred by recent achievements in the application of neural networks combined with search algorithms [3, 4], learning from breakthroughs in their application to games such as chess and Go [5]. Recent studies have shown that neural network policies framed as multi-class classification problems can identify likely reactions through the noisy knowledge base [3, 4]. We explore and tune neural network architectures
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