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https://hdl.handle.net/2115/84935

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

英語

reaction conditions

2021-12-27

MDPI

International Journal of Molecular Sciences

23(1)

248

The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (similar to 42,000 reactions) from Reaxys(R) database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

学術雑誌論文

Version of Record

1422-0067