Protein-ligand affinity prediction method based on interaction energy terms and machine learning

Abstract

The present invention relates to a protein-ligand affinity prediction method based on interaction energy terms and machine learning. The method is characterized by dispersing the various interaction energy terms of a ligand and a protein pocket onto the main side chains of 20 amino acid residues; training the protein-ligand interaction energy information with known activity in a PDB library by a machine learning method; and scoring the ligand and protein affinity by using the obtained model. By dispersing the interaction energy terms, the method can fully consider the influence of the main side chains of different amino acid residues on the affinity, and uses machine learning to perform nonlinear fitting so as to contribute to processing the correlation or coupling between interaction energies, thereby reducing errors caused by different amino acid structures in the calculation of affinity. The method contributes to the prediction of the affinity of active molecules so as to achieve apurpose of improving prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of scoring functions, in particular to a method for predicting protein-ligand affinity based on interaction energy between ligand and protein binding pocket residues and machine learning. Background technique [0002] In the process of drug design, scoring functions are usually used to predict the binding affinity between protein targets and their ligands, thereby improving the success rate of drug design and reducing the cost of drug screening. In recent years, it has been paid more and more attention by relevant scientific research institutions and pharmaceutical companies. Common scoring function methods can be divided into force field-based scoring functions, experience-based scoring functions and knowledge-based scoring functions. Commonly used scoring functions include PLP, ChemScore, X-Score, and GlideScore, etc. Among them, the scoring function based on experience is the most widely used. Empirica...

AI Technical Summary

Problems solved by technology

However, the current scoring function based on experience still has related problems such as poor correlation between predicted value and experimental value, large target dependence and poor sensitivity to congeners

There are many reasons for these problems. For example, too few interaction energy items cause the differentiation to be ignored to a certain extent. The protein-ligand complex data set is rarely enough to cause insufficient diversity. Linear regression ignores the coupling effect between each interaction energy item.

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