Organic Compound 3D Structure Prediction with Adjustable Features
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Solution Overview
Problem
Conventional machine learning models for predicting atomic structures, such as AlphaFold2, only output one typical three-dimensional structure and do not enable the prediction of diverse three-dimensional structures.
Innovation Solution
A prediction control process that adjusts intermediate feature values of a structure prediction model, such as AlphaFold2, to align with actual three-dimensional density maps, using techniques like backpropagation and differentiable conversions, to predict diverse three-dimensional structures.
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 limited
Solution Approach 1:
The patent applies dynamics by making the intermediate feature values adjustable and controllable during the prediction process. 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 machine learning model. By adjusting these internal parameters, the system can steer the prediction toward different three-dimensional structures while maintaining the efficiency of the original model architecture.
2Adaptability or versatility
If the structure prediction model is modified to predict diverse structures, then the versatility improves, but the model complexity increases
Solution Approach 1:
The patent extracts only the necessary intermediate feature values from the machine learning model for modification, rather than restructuring the entire model. This selective approach allows diversity in predictions while keeping the overall model architecture relatively simple and manageable.
Solution Approach 2:
The patent applies partial action by modifying only specific intermediate features rather than the entire model. This targeted modification achieves the goal of structure diversity without requiring comprehensive model restructuring, thus avoiding excessive complexity.
3Measurement precision
If intermediate feature values are adjusted to match density maps, then the prediction accuracy improves, but the computational cost increases
Solution Approach 1:
The patent performs preliminary action by pre-adjusting intermediate feature values based on density map information before final structure prediction. This upfront adjustment reduces the need for extensive iterative optimization, thereby improving accuracy while controlling computational energy expenditure.
Solution Approach 2:
The patent implements feedback by using density map information to guide adjustments of intermediate feature values. This feedback mechanism ensures that modifications are directed toward accurate structures, improving precision while avoiding wasteful computational exploration of incorrect configurations.
Data Source
AI summary
A non-transitory computer-readable recording medium has stored therein a prediction control program that causes a computer to execute a process. The prediction control program is a prediction control program of a structure prediction model that predicts a three-dimensional structure of an organic compound from sequence information on the organic compound. The process comprises changing an intermediate feature value of the structure prediction model so that a difference between first limiting information and second limiting information different from the first limiting information is lessened, the first limiting information corresponding to a predicted structure output as a prediction result from the structure prediction model.