Machine Learning Catalyst Prediction via Variational Autoencoder
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Solution Overview
Problem
Current catalyst design in asymmetric catalysis faces challenges due to the lack of mechanistic understanding, limitations in recognizing patterns in large data sets, and the absence of quantitative guidelines, making it difficult to accurately predict selective catalysts, especially beyond the bounds of training data.
Innovation Solution
A chemoinformatics-guided workflow using average steric occupancy (ASO) and electronic descriptors to create a universal training set of catalysts, enabling the prediction of enantioselective reactions by selecting optimal catalysts through machine learning methods, which covers a broad range of feature space and captures subtle catalyst structures responsible for enantioinduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning methods are used to predict enantioselectivity, then the ability to identify selective catalysts is improved, but the accuracy of predictions beyond training data bounds deteriorates
Solution Approach 1:
The patent transforms the prediction problem from selecting among existing catalysts to generating entirely new catalyst structures by adding a generative dimension. The variational autoencoder learns the latent space of catalyst structures and enables navigation to regions corresponding to high enantioselectivity that were not present in the training data, thus resolving the contradiction between prediction accuracy and extrapolation capability.
Solution Approach 2:
The patent performs preliminary generation and screening of catalyst candidates in silico before experimental validation. The generative model pre-identifies promising catalyst structures with predicted high enantioselectivity, allowing experimentalists to focus resources on the most promising candidates and achieving better extrapolation beyond training data.
2Reliability
If a large number of catalyst candidates are evaluated, then the probability of finding highly selective catalysts is improved, but the time and resources required for screening increase
Solution Approach 1:
The patent creates a virtual copy of the catalyst screening process through the variational autoencoder model. Instead of physically synthesizing and testing numerous catalyst candidates, the model generates and evaluates virtual catalyst structures in silico, dramatically reducing the time and resources required while maintaining the ability to identify highly selective catalysts.
Solution Approach 2:
The patent performs preliminary in silico screening of generated catalyst candidates before experimental validation. The variational autoencoder pre-identifies promising catalyst structures based on learned structure-activity relationships, allowing experimentalists to prioritize the most promising candidates and significantly reducing overall screening time and resources.
3Loss of information
If traditional Edisonian empiricism is used for catalyst design, then mechanistic understanding is maintained, but the ability to find patterns in large data sets deteriorates
Solution Approach 1:
The patent replaces the mechanical process of human pattern recognition with a computational variational autoencoder model. The neural network automatically learns complex patterns in catalyst structure-enantioselectivity relationships from large datasets, overcoming the limitations of human cognitive processing while maintaining interpretability through visualization of the latent space and generated structures.
Data Source
AI summary
Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.