Variational Autoencoder Catalyst Translation Network
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating catalysts, especially enzymatic catalysts, are limited by thermodynamic constraints and require significant investment of time and capital, making it difficult to produce catalysts for the vast majority of possible small molecule organic chemicals, and existing approaches like manual modeling and directed evolution are inefficient and costly.
Innovation Solution
The use of Variational AutoEncoders for generative modeling to construct catalyst features from chemical features, enabling the joint distribution of latent spaces for reactants and catalysts, allowing for the generation of catalysts from any set of chemical features through multi-task learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modeling or directed evolution is used to generate catalysts, then catalyst specificity can be achieved, but the process requires large investment of capital and time, greatly increasing cost and reducing scalability
Solution Approach 1:
The patent uses generative modeling to create virtual copies of catalysts through algorithmic translation between chemical and catalyst feature spaces. Instead of manual design or iterative evolution, the system generates catalyst candidates by decoding latent vectors from trained autoencoders, dramatically reducing the time and capital required while maintaining the ability to achieve catalyst specificity for target reactions
Solution Approach 2:
The patent replaces manual modeling and directed evolution mechanisms with an automated machine learning system. The variational autoencoder framework with cyclic translation between chemical and catalyst spaces substitutes human intervention and iterative biological evolution with algorithmic generation, reducing both time investment and capital costs while preserving catalyst specificity
2Manufacturing precision
If manual modeling of enzyme structure is performed, then detailed enzyme design is possible, but manual intervention is required for each new enzyme, greatly increasing cost and reducing scalability
Solution Approach 1:
The patent creates a universal machine learning model that can generate catalysts for any target reaction by learning the joint distribution between chemical and catalyst feature spaces. The trained variational autoencoder system serves multiple functions: it can translate from any chemical features to corresponding catalyst features, eliminating the need for separate manual modeling efforts for each new enzyme while maintaining detailed structural design capability
Solution Approach 2:
The system uses generative modeling to create virtual copies of enzyme structures through algorithmic translation. Instead of manual intervention for each new enzyme, the trained model generates catalyst candidates by decoding latent vectors, preserving detailed manufacturing precision while enabling scalable production across numerous different enzyme designs
3Adaptability or versatility
If directed evolution is used to generate new catalysts, then novel catalysts can be discovered, but expensive manual intervention is required to choose candidate enzymes
Solution Approach 1:
The patent implements self-service by enabling the machine learning model to autonomously generate and evaluate catalyst candidates without expensive manual intervention. The variational autoencoder system automatically translates chemical features to catalyst features, performs cyclic validation, and generates novel catalyst discoveries through algorithmic exploration of the latent space, eliminating the need for human experts to manually choose candidate enzymes while maintaining adaptability and versatility
4Reliability
If existing enzymatic catalysts are used, then core metabolic functions are covered, but the vast majority of possible small molecule organic chemicals remain unreachable
Solution Approach 1:
The patent creates a universal catalyst generation system that can produce catalysts for any target reaction by learning the joint distribution between chemical and catalyst feature spaces. The model is trained on diverse datasets and can generalize to novel chemical transformations, enabling the system to cover the vast majority of possible small molecule organic chemicals while maintaining reliable catalyst functionality through the continuous latent space and cyclic translation validation
Data Source
AI summary
The present invention provides a computer system for generating the molecular structure of a catalytic activator for a reaction in which input reactants, a.k.a. substrates, are converted into an output product, the computer system comprising: a trained machine learning model, preferably a variational autoencoder, configured to receive an operating feature set defining chemical features of the input reactants and chemical features of the output product of a reaction and to generate therefrom a set of catalyst features defining one or more catalytic activator, which is preferably an enzyme, for catalysing a reaction to convert the input reactants to the output product.