Molecule Synthesizability Prediction Using Retrosynthesis Features
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Solution Overview
Problem
Existing methods for predicting the synthesizability of molecules are computationally expensive and require large amounts of labeled training data, which is scarce, and direct neural network approaches are complex and resource-intensive.
Innovation Solution
A two-stage machine learning approach involving a retrosynthesis featurization model to generate a feature array and a synthesizability prediction model to produce a synthesizability score, leveraging a smaller neural network architecture and using programmatically assembled retrosynthesis trees for training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct neural network approaches are used to predict synthesizability, then prediction capability is achieved, but computational resources and model complexity increase significantly
Solution Approach 1:
The patent divides the synthesizability prediction task into two separate stages: (1) a retrosynthesis featurization model that processes molecular input and generates retrosynthesis tree features, and (2) a synthesizability prediction model that takes those features as input to predict synthesizability scores. This segmentation allows each model to be simpler and more specialized, reducing overall complexity while maintaining prediction accuracy.
2Measurement precision
If existing methods are used to predict synthesizability, then prediction results are obtained, but computational cost and time consumption increase
Solution Approach 1:
The patent pre-computes and stores retrosynthesis tree features for molecular inputs during the featurization stage. These pre-computed features serve as ready-to-use inputs for the prediction model, eliminating the need to generate retrosynthesis trees during actual prediction tasks. This preliminary action significantly speeds up prediction while maintaining accuracy.
3Measurement precision
If large amounts of labeled training data are used, then model training accuracy improves, but data availability decreases due to scarcity
Solution Approach 1:
The patent introduces retrosynthesis tree features as an intermediary representation between molecular input and synthesizability prediction. These features capture essential structural and chemical information in a compressed form, allowing the model to learn effective patterns with fewer labeled examples. The intermediary features act as a bridge that reduces the dependency on large labeled datasets.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting synthesis complexity. In one aspect, a method comprises: receiving data characterizing a molecule; processing a model input that comprises the data characterizing the molecule, using a retrosynthesis featurization machine learning model and in accordance with values of a set of retrosynthesis featurization machine learning model parameters, to generate a feature array comprising a plurality of features of a retrosynthesis tree for the molecule; and processing a model input that comprises the feature array comprising the plurality of features of the retrosynthesis tree for the molecule, using a synthesizability prediction machine learning model and in accordance with values of a set of synthesizability prediction machine learning model parameters, to generate a synthesizability score that characterizes a synthesis complexity of the molecule.