Machine learning approach to discovering cascade reaction patterns. Application to reaction pathways prediction.
We propose a combinatorial learning procedure for discovering graph transformation patterns based on combining transformations that can be used in a consecutive fashion. Application of this kind of pattern to a specified chemical system allows to combine a sequence of consecutive transformations into a one-step operation limiting the complexity of a reaction tree. In a retrosynthetic sense, it provides a global strategy for bond disconnections to plan more efficient convergent syntheses. In a forward direction the approach reduces the number of iterations in order to exploring the courses of complex multistep processes. The procedure enhances the capabilities and applications of the CSB (Chemical Sense Builder) computer program to assist in organic synthesis, biochemistry, or medicinal chemistry. As an example, we present the automatic derivation of the transformation pattern for Ugi-type four-component reactions (reaction sequences) and its application to assist in the designing new cascade transformations for diversity-oriented synthesis (DOS).
Department of Physical Chemistry, Faculty of Chemistry, Rzeszow University of Technology, 35-959 Rzeszow, Poland. gnowak@prz.edu.pl
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D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics; D020650:Combinatorial Chemistry Techniques; D008956:Models, Chemical