Compound-Protein Interaction Models for Flexible Bioactivity Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning systems for predicting biological interactions between compounds and proteins suffer from inaccuracies, inefficiencies, and operational inflexibility, particularly in generating bioactivity predictions and identifying underlying biological drivers.
Innovation Solution
A protein interaction learning system utilizes a compound-protein interaction machine learning model to generate a machine learning representation, which trains target machine learning models to predict bioactivity results and identifies contributing proteins through explainability models, employing techniques like clustering, normalization, and similarity measures to improve accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning systems use large volumes of training data to predict biological interactions, then they can generate predictions, but they suffer from inaccuracies and inefficiencies
Solution Approach 1:
The patent introduces compound-protein machine learning representations as an intermediary layer between raw training data and final bioactivity predictions. These representations encode interaction patterns in a compressed format, serving as a mediator that improves prediction accuracy while reducing the computational burden of processing large volumes of raw data.
Solution Approach 2:
The system transforms the parameter space by converting raw compound-protein interaction data into machine learning representations with optimized dimensionalities. This parameter transformation enables more accurate predictions by capturing essential interaction patterns while reducing the complexity of the original high-dimensional data.
2Adaptability or versatility
If conventional systems implement machine learning models for bioactivity predictions, then they can generate predictions, but they lack operational flexibility
Solution Approach 1:
The compound-protein machine learning representations serve multiple functions: they can be used for training target machine learning models, generating predictions for various bioactivity endpoints, and providing explainability insights. This multi-functionality increases operational flexibility without proportionally increasing system complexity.
Solution Approach 2:
The system performs preliminary action by pre-computing compound-protein machine learning representations that capture essential interaction patterns. These pre-computed representations can then be reused across different prediction tasks and models, reducing the need for retraining and improving operational flexibility.
3Loss of information
If the system generates detailed bioactivity predictions and identifies contributing proteins, then it provides comprehensive insights, but it requires extensive computational resources
Solution Approach 1:
The patent extracts only the most relevant information from compound-protein interactions by generating machine learning representations that capture essential patterns. This extraction approach provides sufficient biological mechanism identification without processing all raw interaction data, thereby reducing computational resource consumption.
Solution Approach 2:
The system applies partial action by using a subset of the full interaction data space through machine learning representations. These representations capture the necessary biological mechanisms while excluding redundant information, achieving effective protein contribution identification with reduced computational effort.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and/or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.