Organic Compound Structure Prediction via Density-Map Feature Control
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Solution Overview
Problem
Conventional machine learning models for predicting protein structures, such as AlphaFold2, only output one typical atomic structure and fail to predict diverse three-dimensional structures, limiting their application in drug development.
Innovation Solution
A prediction control program that adjusts intermediate feature values of a structure prediction model to minimize the difference between predicted and actual three-dimensional density maps, enabling the model to output diverse three-dimensional structures by using differentiable conversions and backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model outputs one typical three-dimensional structure, then the prediction is simple and fast, but the diversity of predicted structures is insufficient
Solution Approach 1:
The patent applies dynamics by making the intermediate feature values adjustable and controllable. Instead of fixed intermediate features, the system allows dynamic modification of these features to guide the model toward different structural outcomes, enabling both speed and diversity
Solution Approach 2:
The patent changes parameters by modifying intermediate feature values within the neural network. By adjusting these intermediate parameters, the system can generate diverse three-dimensional structures while maintaining efficient prediction through controlled parameter variation rather than complete retraining
2Adaptability or versatility
If the model predicts diverse three-dimensional structures, then the application value for drug development increases, but the prediction complexity and computational cost increase
Solution Approach 1:
The patent applies preliminary action by pre-training the model to learn meaningful intermediate feature representations. This pre-learning phase enables the model to quickly generate diverse structures later by simply adjusting intermediate features, avoiding the need for complex retraining procedures
Solution Approach 2:
The patent uses intermediate feature values as mediators between the input amino acid sequence and the final three-dimensional structure. These intermediate features act as controllable adjustment points that enable structure diversity without requiring changes to the overall model architecture or training procedure
Data Source
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AI summary
A prediction control program is a prediction control program for a structure prediction model 11 that predicts a three-dimensional structure of an organic compound from sequence information on the organic compound. The prediction control program causes a computer to execute a process of changing an intermediate feature value of the structure prediction model 11 so that a difference between a three-dimensional density map m1 and a three-dimensional density map m0 different from the three-dimensional density map m1 and actually measured is lessened, the three-dimensional density map m1 corresponding to a predicted structure output as a prediction result from the structure prediction model 11.