Collaborative anti-tumor multi-drug combination effect prediction method based on deep learning

Abstract

The invention provides a collaborative anti-tumor multi-drug combination effect prediction method based on a deep learning algorithm and pharmacogenomics. The collaborative anti-tumor multi-drug combination effect prediction method comprises the following steps: (1) mining and preprocessing large-scale pharmacogenomics data; (2) effectively integrating different feature information and constructing a modeling sample; (3) constructing a collaborative anti-tumor multi-drug combination prediction model based on large-scale sample data and a deep learning algorithm; and (4) performing parameter optimization and performance improvement of the model. According to the method, an artificial intelligence deep learning algorithm and pharmacogenomics are effectively combined, the limitation that a traditional collaborative drug combination prediction method can only be used for predicting the synergistic effect between every two drugs is overcome, and the specific collaborative anti-tumor multi-drug combination can be screened out for different tumor cells through the gene level; therefore, theoretical basis and technical support are provided for solving the problem of tumor drug resistance,and more effective treatment schemes are further provided for clinical tumor treatment.

Description

technical field [0001] The present invention relates to the field of computer-aided drug screening, in particular to a method for predicting the effect of synergistic anti-tumor multi-drug combinations based on deep learning and pharmacogenomics, which is suitable for different drugs based on tumor cell gene expression data and drug target information. Tumor cells screen out specific drug combinations with synergistic anti-tumor effects. Background technique [0002] Cancer is a major disease that seriously threatens human life and health, and its mortality rate ranks second only to cardiovascular diseases. The main treatment methods for tumors are surgical treatment, radiotherapy and drug therapy. At present, drug therapy is still an important means of tumor treatment. Due to the variety of tumor pathogenic factors, its development process is complex and regulated by many factors, the treatment of a single drug can easily cause the human body to develop a drug-resistant ph...

AI Technical Summary

Problems solved by technology

[0003] With the development of information technology, domestic and foreign scholars have begun to try to establish a machine learning calculation model based on compound structure information, and predict the combination of synergistic anti-tumor drugs by calculating the similarity between compound structures, but they often face the following problems: 1 ) This method is only suitable for the prediction of synergy between two drugs, and cannot predict the synergy between three or more drugs; 2) lack of sufficient modeling data, the prediction accuracy of new synergistic drug combinations Poor; 3) It is impossible to screen out specific synergistic drug combinations for a given tumor cell; 4) Traditional machine learning methods cannot automatically learn feature information from big data, requiring a lot of manual feature selection

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