Biomolecular Structure Prediction Using Interaction-Guided Sampling

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current methods for predicting the structure of biomolecular compounds, such as AlphaFold3, suffer from random and inefficient sampling techniques, leading to low accuracy and high computational inefficiency due to repeated sampling of similar conformations and difficulty in capturing the correct conformation.

Innovation Solution

A method that involves simulating potential interaction patterns between biomolecular sequences, predicting a probability distribution for candidate structural unit groups, filtering based on similarity and probability thresholds, and optimizing the structure prediction model using labeled and unlabeled sampling to select the most likely and diverse structural unit groups.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If random sampling techniques are used for structure prediction, then the method is simple to implement, but the accuracy is low and computational efficiency is poor

Engineering Contradiction:

Improvestructure prediction accuracyVSAvoidcomputational efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent applies preliminary action by performing pre-sampling to generate an initial set of conformations before the main prediction process. This pre-sampling step creates a foundation of candidate structures that guides subsequent sampling, avoiding random exploration and focusing computational resources on promising regions of the conformational space, thereby improving both accuracy and efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where sampling results are continuously evaluated and used to adjust subsequent sampling strategies. The system analyzes predicted structures, identifies successful sampling patterns, and uses this information to guide future sampling decisions, creating a closed-loop system that improves accuracy while maintaining computational efficiency through intelligent adaptation

Inventive Principle:

Principle #23Feedback

2Reliability

If repeated sampling of similar conformations is performed, then more comprehensive coverage is achieved, but computational time increases significantly

Engineering Contradiction:

Improveconformation coverageVSAvoidcomputational time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent extracts and removes redundant conformations from the sampling process by implementing filtering mechanisms that identify and eliminate duplicate or highly similar structures. This extraction of unnecessary computational work maintains comprehensive coverage of unique conformations while significantly reducing computational time by avoiding repeated processing of identical structures

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent changes sampling parameters dynamically based on the exploration progress. By adjusting sampling density, temperature, and other parameters according to the diversity of generated conformations, the system achieves thorough coverage when needed while reducing sampling intensity when sufficient diversity is obtained, optimizing the balance between reliability and time efficiency

Inventive Principle:

Principle #35Parameter changes

3Reliability

If the model predicts all candidate structural unit groups, then completeness is improved, but computational complexity increases

Engineering Contradiction:

Improveprediction completenessVSAvoidmodel computational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the prediction task by dividing candidate structural unit groups into different priority levels or categories. Instead of treating all candidates equally, the model processes high-probability or biologically relevant groups first, using hierarchical segmentation to maintain completeness while reducing immediate computational complexity through staged processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial action by predicting and evaluating only the most promising candidate structural unit groups rather than exhaustively processing all possibilities. By focusing computational resources on a subset of high-value candidates identified through preliminary scoring or filtering, the system achieves sufficient completeness for practical applications while significantly reducing model complexity

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260094671A1Method for predicting structure of compound model, method for training model, and related apparatuses

Publication Date: 2026.04.02 BEIJING BAIDU NETCOM SCI & TECH CO LTD

AI summary

A method for predicting a structure of a compound includes: obtaining a combination of biomolecular sequences by combining specified biomolecular sequences; predicting a first probability distribution for the combination of biomolecular sequences, in which the first probability distribution is used for indicating first probabilities of candidate structural unit groups in the combination of biomolecular sequences, a candidate structural unit group includes structural units of at least two biomolecular sequences, and a first probability is used for indicating a possibility of interaction between structural units in a respective candidate structural unit group; determining, from the plurality of candidate structural unit groups, at least one first structural unit group based on the first probability distribution; and predicting a target structure of a biomolecular compound based on structural units interacted with each other in the at least one first structural unit group.