Affinity Prediction Model for Protein-Compound Binding
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Solution Overview
Problem
Conventional methods for predicting the affinity between protein targets and compound molecules are costly and inefficient, especially when dealing with vast numbers of compound structures, as they require extensive in-vitro experiments to determine binding affinity.
Innovation Solution
A method and apparatus for training an affinity prediction model using a large dataset that includes information of training targets, training drugs, and test data sets, allowing for the prediction of affinity between target proteins and compound molecules, thereby reducing the need for extensive experimental verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-vitro activity experiments are performed on compound molecules to accurately detect affinity between drug and protein target, then measurement precision of affinity is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training an affinity prediction model using historical experimental data and protein structural information before actual affinity detection. This pre-computed knowledge base enables rapid prediction without requiring time-consuming in-vitro experiments for each new compound-protein pair, thus resolving the contradiction between measurement precision and time consumption
Solution Approach 2:
The patent uses computational copying by creating virtual representations of protein structures and compound molecules, then simulating their interactions through machine learning models. These digital copies allow affinity prediction without physical experimentation, maintaining measurement precision through validated models while eliminating the time and resource costs of actual experiments
2Measurement precision
If in-vitro activity experiments are performed on compound molecules to accurately detect affinity, then measurement precision is improved, but loss of cost increases significantly
Solution Approach 1:
The patent replaces expensive physical experiments with computational copies of protein structures and molecular interactions. By using pre-trained machine learning models that have been validated against historical experimental data, the system achieves accurate affinity predictions without incurring the high costs of repeated in-vitro experiments, thus resolving the contradiction between measurement precision and cost
Solution Approach 2:
The patent changes the fundamental parameter of affinity detection from physical measurement to computational prediction. By transforming the detection method from experimental to algorithmic, the system maintains measurement precision through validated models while dramatically reducing the energy and financial costs associated with physical experimentation
3Reliability
If conventional experimental methods are used to screen compound molecules, then reliability of affinity data is improved, but productivity decreases due to inability to handle vast compound space
Solution Approach 1:
The patent applies preliminary action by pre-training the affinity prediction model on extensive historical experimental data and protein structural information before deployment. This pre-computed knowledge enables the system to reliably screen vast compound spaces at high speed, resolving the contradiction between data reliability and screening productivity
Solution Approach 2:
The patent uses computational copying to create virtual models of protein structures and molecular interactions, allowing rapid in-silico screening of millions of compounds. These digital copies maintain reliability through validation against experimental data while enabling high-throughput screening that physical methods cannot achieve
Data Source
AI summary
The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.