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

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 struct...

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Bibliographic Details
Published in:International journal of molecular sciences 2021-12, Vol.23 (1), p.248
Main Authors: Afonina, Valentina A, Mazitov, Daniyar A, Nurmukhametova, Albina, Shevelev, Maxim D, Khasanova, Dina A, Nugmanov, Ramil I, Burilov, Vladimir A, Madzhidov, Timur I, Varnek, Alexandre
Format: Article
Language:English
Subjects:
Automation
Catalysts
Datasets
Hydrogenation
Information retrieval
Likelihood Functions
Models, Chemical
Neural networks
Predictions
Ranking
Reagents
Selectivity
Solvents
Sparsity
Stereoisomerism
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Summary: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, (~42,000 reactions) from Reaxys database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms23010248