Delta Model for Molecular Property Difference Prediction
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Solution Overview
Problem
Current molecular machine learning algorithms are not optimized to directly compare molecular properties, guide molecular derivatizations, or design prodrugs effectively, limiting their ability to prioritize lead series and predict property differences for enhanced pharmacokinetic control and innovative release mechanisms.
Innovation Solution
A computer-implemented method for training a machine learning model to predict molecular property differences by creating pairs of molecules, generating shared molecular representations, and using these pairs to train the model, allowing for the prediction of property differences and improvements in molecular derivatizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular machine learning algorithms predict absolute property values from chemical structure, then prediction accuracy improves with expanding training data and computational power, but the algorithms are not optimized to directly compare molecular properties or predict property differences for molecular derivatizations
Solution Approach 1:
The patent segments the prediction task into two distinct components: (1) a base model that predicts absolute property values of parent molecules, and (2) a delta model that predicts property differences for molecular derivatizations. This segmentation allows each model to be optimized for its specific function, with the delta model trained specifically on property difference data to directly compare molecular properties and guide derivatizations.
Solution Approach 2:
The patent introduces an intermediary delta model that acts as a mediator between the base molecular representation and the final property difference prediction. This delta model takes as input the base molecular representation and molecular modification information, then outputs the predicted property difference, enabling direct comparison of molecular properties without requiring full re-prediction of absolute values.
2Reliability
If prodrugs are designed with increased complexity to enable greater pharmacokinetic control and innovative release mechanisms, then pharmacokinetic optimization improves, but rational prodrug design becomes more challenging
Solution Approach 1:
The patent applies preliminary action by using the trained delta machine learning model to predict property differences for various prodrug designs before actual synthesis and testing. This allows researchers to virtually evaluate multiple prodrug candidates with different complexities, predicting their pharmacokinetic properties in advance, and select the most promising candidates for further development, thereby reducing the challenges of rational prodrug design.
3Productivity
If current molecular machine learning algorithms are used for lead series prioritization, then experimental testing can be triaged, but the algorithms cannot directly compare molecular properties to guide molecular derivatizations
Solution Approach 1:
The patent replaces the mechanical approach of performing sequential experimental testing and analysis with a computational delta model that directly predicts property differences. Instead of physically synthesizing and testing multiple derivatives to compare properties, the delta model computationally substitutes for this process by taking molecular structure inputs and directly outputting predicted property differences, thereby guiding molecular derivatizations in silico before experimental validation.
Data Source
AI summary
Described herein are methods for the direct comparison of predicted properties of molecular derivatives for molecular optimization, lead series prioritization, and computational design of prodrugs that exhibit desired biological and physical properties. The described pipeline can be used to streamline the optimization of drug leads and design of prodrugs for small molecular FDA-approved drugs and investigational preclinical drug candidates.