Drug synergistic effect prediction method based on biomedical knowledge graph and Transform

Abstract

The invention discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and Transform, and relates to a drug synergistic effect prediction method. The invention aims to solve the problems that the traditional drug combination discovery process mainly depends on clinical tests, time and labor are wasted, the cost is high, and the patient also has risks. The method comprises the steps of extracting a drug data sample, generating a data set, dividing a training set and a test set, and performing network training and testing. Constructing a biomedical knowledge graph; converting the sequence representation of the drug structure into a graph by using Rdkit; mining sub-graphs of the knowledge graph by using the multi-hop sub-graph mining network; using a relation perception Transform to learn the feature representation of the knowledge network and the drug molecular map, and fusing the feature representation of the knowledge network and the feature representation of the drug molecular map; a multi-layer perceptron is used for predicting the synergistic effect between the drug pairs; inputting the drug pairs in the training set into the model; and inputting the drug pairs in the test set into the prediction model to obtain a prediction result. The invention belongs to the technical field of drug synergistic effect prediction.

Description

Technical Field

[0001] The invention relates to a drug synergistic effect prediction method, belonging to the technical field of drug synergistic effect prediction. Background Art

[0002] In the face of intractable diseases such as cancer, AIDS, and cardiovascular disease, single-drug therapy alone often fails to achieve satisfactory results. These diseases trigger abnormalities in multiple cellular mechanisms and affect tissues and organs throughout the body. In the field of cancer treatment, combination drug therapy strategies have become increasingly important, maximizing the therapeutic effect by combining multiple drugs. This approach has been favored for several significant advantages: (1) it can improve the therapeutic effect; (2) it helps overcome drug resistance; and (3) it reduces side effects caused by excessive drug doses. For example, the combination of amiloride and hydrochlorothiazide has been shown to be effective in treating hypertension. Currently, this multi-drug combination therapy has become the standard for treating a variety of complex diseases. However, the traditional drug combination discovery process mainly relies on clinical trials, which are not only time-consuming and labor-intensive, but also costly, and the potential risks to patients cannot be ignored. The emergence of high-throughput screening technology has enabled the accumulation of drug combination data. Researchers can now evaluate the interactions of a large number of drug combinations in various cell lines, thereby accelerating the discovery of new drug combinations. In today's era of booming biomedical data, it is almost impossible to comprehensively test all possible drug combinations. Therefore, it is imperative to develop an efficient and economical drug combination screening method.

[0003] To address this challenge, researchers have developed a range of computational methods to predict the effects of drug combinations, covering areas such as systems biology, mathematical modeling, dynamic simulation, and machine learning. Systems biology methods use biological knowledge to analyze biological networks, while mathematical methods rely on mathematical models and statistical tests. Dynamic models make predictions by simulating the dynamic changes of nodes in biological networks. Although these methods are suitable for processing small-scale data sets, they usually rely on existing knowledge. At the same time, machine learning methods, such as random forests, support vector machines, and naive Bayes, can process large-scale data sets and accelerate the process of identifying drug combinations.

[0004] In recent years, the development of deep learning techniques has brought new opportunities for drug combination prediction. These techniques can extract key features from large-scale data sets and have shown their effectiveness in the biomedical field. However, although these methods have made progress in analyzing the molecular structure of drugs and cell lines, they often ignore the interaction information between drugs.

[0005] As a heterogeneous graph, knowledge graph represents entities and their relationships through nodes and edges, providing a new perspective for discovering potential connections between entities. In recent years, knowledge graph has achieved success in natural language processing, recommendation systems and other fields. Summary of the invention

[0006] In order to solve the problem that the traditional drug combination discovery process mainly relies on clinical trials, which is not only time-consuming and labor-intensive, but also costly and poses risks to patients, the present invention proposes a drug synergy prediction method based on biomedical knowledge graph and Transformer.

[0007] The technical solution adopted by the present invention to solve the above-mentioned problem is: the specific steps of the present invention include:

[0008] Step 1: Extract drug data samples, generate data sets, divide them into training and test sets, and perform network training and testing;

[0009] Step 2: Use drug and other node information to construct a biomedical knowledge graph;

[0010] Step 3: Convert the sequence representation of the drug structure into a graph using Rdkit;

[0011] Step 4: Use the multi-hop subgraph mining network to mine the subgraph of the knowledge graph;

[0012] Step 5: Use the relation-aware Transformer to learn the feature representation of the knowledge network and drug molecule graph and fuse them;

[0013] Step 6: Use a multi-layer perceptron to predict the synergistic effect between drug pairs;

[0014] Step 7: Input the drug pairs in the training set into the above model to train the model; after multiple iterations, the final prediction model is obtained;

[0015] Step 8: Input the drug pairs in the test set into the prediction model, obtain the prediction results, and analyze the results.

[0016] Furthermore, step 1 specifically includes:

[0017] The dataset used was randomly divided into a training set and a test set at a ratio of 4:1, 80% of which was a training set and 20% was a test set. Both the training set and the test set contained each relationship type, and a 5-fold cross-validation process was performed on the dataset.

[0018] Furthermore, step 2 specifically includes:

[0019] The knowledge graph is represented as G = (E, R), where E represents the node set of the knowledge graph and R represents the relationship set of the knowledge graph;

[0020] The structure of the knowledge graph is represented by an entity-relationship-entity triple T = (h, r, t), where h, t∈E represent the head entity and the tail entity respectively, and r∈R represents the relationship between two entities.

[0021] Furthermore, step 4 specifically includes:

[0022] Step 401: Use the shortest path strategy to extract a knowledge subgraph for each drug or cell line, and filter out target nodes that are far from the source node;

[0023] Step 402: Use a K-hop subgraph extractor to construct a subgraph of each node by including related nodes and nodes adjacent to the node;

[0024] Step 403: input the sampled subgraph into the relation-aware graph converter module;

[0025] Step 404: Obtain the representation of the drug or cell line node as:

[0026] h i =MLP(BKG(i),MG(i)),

[0027] h c =BKG(c),

[0028] Among them, h i represents the drug node, h c Indicates cell lines.

[0029] Furthermore, in step 5, there are multiple different paths in the relationship path between two distant nodes, and the shortest path is selected as the relationship between the two nodes:

[0030] Step 501, relation-aware self-attention module;

[0031] Given any graph G = (V, E, R), for any node pair (h, t), there is a triple (h, r, t), where h, t ∈ V, r ∈ R, and the self-attention score is updated by the node features and relationship features;

[0032] Q(T)=f(h)W q ,

[0033] K(T)=f(t)W k ,

[0034]

[0035] Where f(h) = x h +r h→t and f(t)=x t +rt→h It is a trainable matrix used to generate query and key. φ(x t )=W v x t Used to calculate value;

[0036] Step 502: Relation-aware Transformer;

[0037] Use the new self-attention mechanism to build a new Transformer.

[0038] α 1 =α 1-1 +Attention(α 1-1 ),

[0039] X l =MLP(X l-1 ),

[0040] α l Represents the nodes updated by the previous layer;

[0041] Step 503: Fuse features and make predictions;

[0042] For a drug synergistic pair (d i , d j , c k ), and their characterization is obtained through the above description (h i ,h j ,h k ), input it into the multi-layer perceptron for prediction,

[0043] P(i, j, k) = MLP(concat(h i ,h j ,h k )).

[0044] The beneficial effects of the present invention are as follows: the present invention proposes a drug synergy prediction method based on biomedical knowledge graph and Transformer, which can predict whether there is synergy between drug pairs; compared with DeepWalk, GCN, DeepSynergy, KGNN, GraphSynergy and KANSynergy, the model algorithm accuracy proposed by the present invention is better than the above six methods, and the model constructed by the present invention has improvements in ACC, AUPR and AUC. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the present invention;

[0046] Figure 2 This is the framework diagram of the present invention DETAILED DESCRIPTION

[0047] Specific implementation method 1: Figure 1 and Figure 2 As shown in the figure, a drug synergy prediction method based on biomedical knowledge graph and Transformer includes the following specific steps:

[0048] Step 1, extract drug data samples, generate data sets, divide them into training and test sets, and perform network training and testing; randomly divide the used data sets into training sets and test sets in a ratio of 4:1, of which 80% is the training set and 20% is the test set, and both the training set and the test set contain each relationship type, and perform 5-fold cross validation on the data sets;

[0049] Step 2: Use drug and other node information to construct a biomedical knowledge graph;

[0050] The knowledge graph is represented as G = (E, R), where E represents the node set of the knowledge graph and R represents the relationship set of the knowledge graph;

[0051] The structure of the knowledge graph is represented by an entity-relationship-entity triple T = (h, r, t), where h, t∈E represent the head entity and the tail entity respectively, and r∈R represents the relationship between two entities;

[0052] Step 3: Convert the sequence representation of the drug structure into a graph using Rdkit;

[0053] Step 4: Use the multi-hop subgraph mining network to mine subgraphs of the knowledge graph; specifically, it includes:

[0054] Step 401: Use the shortest path strategy to extract a knowledge subgraph for each drug or cell line, and filter out target nodes that are far from the source node;

[0055] Step 402: Use a K-hop subgraph extractor to construct a subgraph of each node by including related nodes and nodes adjacent to the node;

[0056] Step 403: input the sampled subgraph into the relation-aware graph converter module;

[0057] Step 404: Obtain the representation of the drug or cell line node as:

[0058] h i =MLP(BKG(i),MG(i)),

[0059] h c =BKG(c),

[0060] Among them, h irepresents the drug node, h c indicates cell lines;

[0061] Step 5: Use the relationship-aware Transformer to learn the feature representation of the knowledge network and the drug molecule graph and fuse them; in the relationship path between two distant nodes, there are multiple different paths, and the shortest path is selected as the relationship between the two nodes:

[0062] Step 501, relation-aware self-attention module;

[0063] Given any graph G = (V, E, R), for any node pair (h, t), there is a triple (h, r, t), where h, t ∈ V, r ∈ R, and the self-attention score is updated by the node features and relationship features;

[0064] Q(T)=f(h)W q ,

[0065] K(T)=f(t)W k ,

[0066]

[0067] Where f(h) = x h +r h→t and f(t)=x t +r t→h It is a trainable matrix used to generate query and key. φ(x t )=W v x t Used to calculate value;

[0068] Step 502: Relation-aware Transformer;

[0069] Use the new self-attention mechanism to build a new Transformer.

[0070] α 1 =α l-1 +Attention(α l-1 ),

[0071] X l =MLP(X l-1 ),

[0072] α l Represents the nodes updated by the previous layer;

[0073] Step 503: Fuse features and make predictions;

[0074] For a drug synergistic pair (di , d j , c k ), and their characterization is obtained through the above description (h i ,h j ,h k ), input it into the multi-layer perceptron for prediction,

[0075] P(i, j, k) = MLP(concat(h i ,h j ,h k ));

[0076] Step 6: Use a multi-layer perceptron to predict the synergistic effect between drug pairs;

[0077] Step 7: Input the drug pairs in the training set into the above model to train the model; after multiple iterations, the final prediction model is obtained;

[0078] Step 8: Input the drug pairs in the test set into the prediction model, obtain the prediction results, and analyze the results.

[0079] Among them, other nodes used include: target protein, cell line, tissue, and the heterogeneous information used includes: drug-protein interaction information, protein-protein interaction information, protein-cell line interaction information, and cell line-tissue interaction information.

[0080] Among them, SMILES, the full name of which is Simplified Molecular Input Line Entry System, is often used to represent drug molecules using SMILES sequences. RDKit is a commonly used biochemical information toolkit. It provides a large number of 2D or 3D computational operations on chemical molecules, can generate molecular descriptors for deep learning, and uses Rdkit to convert the sequence representation of drug molecules into a three-dimensional graph representation.

[0081] This implementation takes into account the acquisition of knowledge graphs, DrugCombDB and Oncology-Screen datasets, which contain the following contents, as shown in Table 1. The DrugCombDB dataset contains 475 drugs, 76 cell lines, and 17,404 synergistic pairs, and the Oncology-Screen dataset contains 21 drugs, 29 cell lines, and 1044 synergistic pairs. Other biomedical knowledge data are obtained from multiple databases such as DrugBank, BindingDB, ChEMBL, PharmGKB, CCLE, and human protein-protein interactome.

[0082] As shown in Table 1, the DrugCombDB dataset contains 16,832 nodes, 4 types of relations, and 250,256 triples in total. The Oncology-Screen dataset contains 16.026 nodes, 4 types of relations, and 226,475 triples in total.

[0083] Table 1

[0084]

[0085] Table 2

[0086]

[0087] Evaluation indicators

[0088] The drug pair interaction prediction method involved in the present invention is a binary classification problem. In the present invention, ACC, AUC and AUPR scores are used as evaluation indicators. The calculation formulas of these indicators are as follows:

[0089] ACC

[0090] Precision is used to measure the proportion of correctly classified samples among all positive and negative samples. The calculation formula is as follows:

[0091]

[0092] The definitions of TP, TN, FP, and FN are shown in Table 3.

[0093] Table 3

[0094]

[0095] AUC

[0096] AUC stands for Area under curve, which is the area under the ROC curve. ROC can reflect the classification ability. Its horizontal axis is the false positive rate (FPR) and the vertical axis is the true positive rate (TPR). The closer the AUC is to 1, the better the model result.

[0097] AUPR

[0098] AUPR stands for Area under Precision / Recall curve, which is the area under the PR curve. The horizontal axis of the PR curve is the recall rate, and the vertical axis is the precision rate. The PR curve is easily affected by the sample distribution (the ratio of positive and negative samples in the training sample), so AUPR can be used to measure the prediction performance of unbalanced data sets. The closer the AUPR value is to 1, the better the model performance.

[0099] The experimental results evaluate the present invention and compare it with DeepWalk, GCN, DeepSynergy, KGNN, GraphSynergy and KANSynergy. The results are shown in Table 4:

[0100] Table 4

[0101]

[0102] The results show that the algorithm accuracy of the model described in the present invention is better than that of the other six methods. Specifically, compared with other methods, the model described in the present invention has improvements in ACC, AUPR, and AUC.

[0103] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A drug synergy prediction method based on biomedical knowledge graph and Transformer, characterized in that: The specific steps include: Step 1: Extract drug data samples, generate data sets, divide them into training and test sets, and perform network training and testing; Step 2: Use drug and other node information to construct a biomedical knowledge graph; Step 3: Convert the sequence representation of the drug structure into a graph using Rdkit; Step 4: Use the multi-hop subgraph mining network to mine the subgraph of the knowledge graph; Step 5: Use the relation-aware Transformer to learn the feature representation of the knowledge network and drug molecule graph and fuse them; Step 6: Use a multi-layer perceptron to predict the synergistic effect between drug pairs; Step 7: Input the drug pairs in the training set into the above model to train the model; after multiple iterations, the final prediction model is obtained; Step 8: Input the drug pairs in the test set into the prediction model, obtain the prediction results, and analyze the results.

2. According to claim 1, a drug synergy prediction method based on biomedical knowledge graph and Transformer is characterized in that: Step 1 specifically includes: The dataset used was randomly divided into a training set and a test set at a ratio of 4:1, 80% of which was a training set and 20% was a test set. Both the training set and the test set contained each relationship type, and a 5-fold cross-validation process was performed on the dataset.

3. The method for predicting drug synergy based on biomedical knowledge graph and Transformer according to claim 1, characterized in that: Step 2 specifically includes: The knowledge graph is represented as G = (E, R), where E represents the node set of the knowledge graph and R represents the relationship set of the knowledge graph; The structure of the knowledge graph is represented by an entity-relationship-entity triple T = (h, r, t), where h, t∈E represent the head entity and the tail entity respectively, and r∈R represents the relationship between two entities.

4. The method for predicting drug synergy based on biomedical knowledge graph and Transformer according to claim 1, characterized in that: Step 4 specifically includes: Step 401: Use the shortest path strategy to extract a knowledge subgraph for each drug or cell line, and filter out target nodes that are far from the source node; Step 402: Use a K-hop subgraph extractor to construct a subgraph of each node by including related nodes and nodes adjacent to the node; Step 403: input the sampled subgraph into the relation-aware graph converter module; Step 404: Obtain the representation of the drug or cell line node as: h i =MLP(BKG(i),MG(i)), h c =BKG(c), Among them, h i represents the drug node, h c Indicates cell lines.

5. The method for predicting drug synergy based on biomedical knowledge graph and Transformer according to claim 1, characterized in that: In step 5, there are multiple different paths in the relationship path between two distant nodes. The shortest path is selected as the relationship between the two nodes: Step 501, relation-aware self-attention module; Given any graph G = (V, E, R), for any node pair (h, t), there is a triple (h, r, t), where h, t ∈ V, r ∈ R, and the self-attention score is updated by the node features and relationship features; Q(T)=f(h)W q , K(T)=f(t)W k , Where f(h) = x h +r h→t and f(t)=x t +r t→h It is a trainable matrix used to generate query and key. φ(x t )=W v x t Used to calculate value; Step 502: Relation-aware Transformer; Use the new self-attention mechanism to build a new Transformer. a l =a l-1 +Attention(a l-1 ), X l =MLP(X l-1 ), α l Represents the nodes updated by the previous layer; Step 503: Fuse features and make predictions; For a drug synergistic pair (d i , d j , c k ), and their characterization is obtained through the above description (h i ,h j ,h k ), input it into the multi-layer perceptron for prediction, P(i,j,k)=MLP(concat(h i ,h j ,h k ))。

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