Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach
Valentina A Afonina1, Daniyar A Mazitov1, Albina Nurmukhametova1
1Chemoinformatics and Molecular Modelling Lab, A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia.
International Journal of Molecular Sciences
|January 11, 2022
Summary
A new Likelihood Ranking Model, an artificial neural network, predicts optimal reaction conditions for chemical synthesis. This model surpasses existing methods in ranking suitability for various reaction components, improving automated synthesis planning.
Area of Science:
- Computational chemistry
- Artificial intelligence in chemistry
- Chemical synthesis optimization
Background:
- Selecting optimal experimental conditions is crucial for automated chemical synthesis planning and execution.
- Classical Quantitative Structure-Property Relationship (QSPR) models have limitations in predicting reaction conditions due to their one-to-one structure-property mapping.
- Reactions can often proceed under multiple viable conditions, necessitating a method that ranks suitability rather than predicting a single optimum.
Purpose of the Study:
- To develop an artificial neural network model that ranks the suitability of various experimental conditions for a given chemical transformation.
- To overcome the limitations of classical QSPR approaches in predicting optimal reaction conditions.
- To provide a scalable and effective tool for automated synthesis planning.
Main Methods:
- Development of the Likelihood Ranking Model, an artificial neural network architecture.
- Training the model on a large dataset of approximately 42,000 hydrogenation reactions from the Reaxys database.
- Benchmarking the model's performance against k Nearest Neighbors and recurrent neural network approaches for predicting reaction condition components (reagents, solvents, catalysts, temperature).
Main Results:
- The Likelihood Ranking Model demonstrated superior performance compared to popular methods like k Nearest Neighbors and recurrent neural networks in assessing reaction conditions.
- The model successfully ranked different conditions based on their suitability for specific chemical transformations.
- Experimental validation confirmed the model's ability to propose effective conditions for reactions, including those with complex selectivity challenges.
Conclusions:
- The Likelihood Ranking Model offers a significant advancement in predicting and ranking optimal reaction conditions for chemical synthesis.
- This artificial neural network approach enhances the automation of synthesis planning by providing a ranked list of suitable conditions.
- The model's effectiveness was validated experimentally, showing its practical utility in complex synthetic scenarios.
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