Drug combination network based drug combined action predicting method

Abstract

The invention discloses a drug combination network based drug combined action predicting method. The method includes: constructing a drug combination network (DCN), and utilizing the DCN to integrate drug synergism combination information, drug-target mutual action information and target protein-protein mutual action information; and mapping targets of two drugs on the DCN respectively, determining an adjacent group of combined acting drugs in the DCN, determining topological network features and biological function relation features of the adjacent group, integrating the topological network features and the biological function relation features of the adjacent group, determining a feature vector of the adjacent group based on integration, establishing a drug combined action effect predicting model based on a support vector machine (SVM), and adopting the support vector machine classification algorithm to predict whether combination of the two drugs generates synergic effects or not. By the method, combined effects of new drugs can be accurately predicted, and the drug combination network based drug combined action predicting method has important value to accelerate development of novel drug combined treatment schemes.

Description

1. Technical field [0001] The invention relates to the field of computer-aided drug molecule design, in particular to a drug combination effect prediction method based on drug combination network. 2. Background technology [0002] The disease treatment effect of single-component molecular targeted drugs usually cannot be maintained for a long time. Although the disease can be controlled to a certain extent in the short term, relapse often occurs after a certain period of time, and it is difficult to achieve the expected curative effect. This is mainly because there are usually multiple complex and redundant signaling regulatory pathways in cells, through which cells can develop drug resistance to tolerate drug treatment. Therefore, multi-component drug combination therapy can more effectively control cell signaling regulatory pathways by simultaneously regulating multiple biological target molecules in the disease network system, and can overcome the limitations of single-ta...

AI Technical Summary

Problems solved by technology

However, the dynamic simulation method is still limited in its wide application in the rational design of drug combinations. This is mainly due to its inherent shortcomings, that is, the kinetic parameters and intermediate product concentration data of a large number of biochemical reaction processes are unknown, resulting in the current dynamic model. limited to very small scale

Therefore, this dynamic model is only suitable for studying the molecular mechanism of action of combination drugs, and is not suitable for promoting the rational discovery of optimal drug combinations

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