Bioisosteric Fragment Replacement for Lower-Toxicity Molecules
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Solution Overview
Problem
Conventional methods for determining the toxicity of molecules, particularly in drug development, require physical synthesis and testing on cell cultures, consuming significant resources and time, and often provide limited understanding of which molecule parts contribute to toxicity.
Innovation Solution
A system using machine learning models predicts toxicity by generating toxicity dose-response curves and toxicity analysis trees, allowing for rapid evaluation of molecular toxicity without physical synthesis, and identifies molecule fragments contributing to toxicity for targeted modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical synthesis and cell culture testing are used to determine molecule toxicity, then accurate toxicity measurements are obtained, but significant resources and time are consumed
Solution Approach 1:
The system performs preliminary toxicity predictions using machine learning models before physical synthesis and cell culture testing. The toxicity prediction model processes molecular structures and generates toxicity scores, allowing researchers to prioritize which molecules need actual testing and reducing the overall number of resource-intensive experiments required.
Solution Approach 2:
The patent creates a computational model that replicates the function of physical toxicity testing. The machine learning model is trained on existing toxicity data and can predict toxicity outcomes for new molecules, serving as a virtual copy of the physical testing process and enabling rapid evaluation without actual cell culture experiments.
2Reliability
If physical synthesis and testing are performed for each molecule, then comprehensive toxicity data is obtained, but time and resource consumption increase significantly
Solution Approach 1:
The system generates toxicity dose-response curves and identifies toxic molecular fragments before physical synthesis. The toxicity analysis tree decomposes molecules into fragments and predicts which fragments contribute to toxicity, allowing researchers to modify molecular structures computationally to reduce toxicity before committing resources to physical synthesis and testing.
Solution Approach 2:
The system modifies molecular parameters by replacing toxic molecular fragments with alternative fragments that have similar structural properties but lower predicted toxicity. The machine learning model evaluates multiple fragment replacement options and selects modifications that optimize toxicity reduction while maintaining molecular function.
3Measurement precision
If conventional toxicity testing methods are used, then toxicity measurements are obtained, but limited understanding of which molecule parts contribute to toxicity is provided
Solution Approach 1:
The toxicity analysis tree decomposes molecules into discrete molecular fragments and evaluates the toxicity contribution of each fragment independently. The system generates a hierarchical representation of the molecule with toxicity scores at each level, identifying which specific fragments are responsible for toxic effects and enabling targeted structural modifications.
Solution Approach 2:
The system applies different toxicity evaluation criteria to different molecular fragments based on their local structural properties and known toxicity patterns. The machine learning model assigns toxicity scores to individual fragments and their interactions, providing localized toxicity information that guides selective fragment replacement rather than treating the entire molecule uniformly.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules. In one aspect, a method comprises: obtaining data identifying: (i) an input molecule, and (ii) a target molecule fragment; determining, for each candidate molecule fragment in a database of candidate molecule fragments, a respective similarity measure between: (i) an embedding of the target molecule fragment, and (ii) an embedding of the candidate molecule fragment; selecting a plurality of candidate molecule fragments for inclusion in a set of alternative molecule fragments based on the similarity measures; and generating data defining a plurality of modified molecules, wherein each modified molecule is a modified version of the input molecule where the target molecule fragment is replaced by a respective alternative molecule fragment from the set of alternative molecule fragments; and generating a respective toxicity prediction for each of the plurality of modified molecules.