Micromolecule screening method and device for targeting microRNA-mRNA interaction
By calculating the target sequence characteristics of mRNA and microRNA ring structure target sequence characteristics and the target probability values of small molecules with high affinity, small molecules with low screening efficiency and poor accuracy in the prior art are solved, and efficient and accurate small molecule screening is achieved.
Patent Information
- Application Number
- CN202311046011.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-18
- Publication Date
- 2025-02-25
AI Technical Summary
The prior art takes a long time to screen small molecules targeting RNA, the number of screening is limited, and there is an off-target effect, which reduces the screening efficiency and accuracy.
By determining the characteristics of the circular structural target sequence formed by mRNA and microRNA, combining the molecular characteristics in the small molecule database, the target probability value was calculated using a convolutional neural network to perform small molecule screening.
The small molecule screening process is simplified, screening efficiency and accuracy are improved, and small molecules with high affinity can be screened in batches and obtained.
Smart Images
Figure CN119517150A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technology, and in particular to a small molecule screening method and device targeting microRNA-mRNA interaction. Background Art
[0002] There are many experimental methods for screening small molecule drugs targeting RNA, such as small molecule microarray screening, high-throughput screening, or nuclear magnetic resonance. The core of these chemical or physical experimental methods is to detect small molecules that can target RNA structures through the interaction between RNA and small molecules. However, the relevant technology takes a long time to screen small molecules, and the number of small molecules that can be screened is limited. In addition, the off-target effect in the experiment will further reduce the efficiency of screening small molecules. Summary of the invention
[0003] In view of this, the present disclosure proposes a small molecule screening method and device targeting microRNA-mRNA interaction, aiming to simplify the small molecule screening process and improve the efficiency and accuracy of the screening results.
[0004] According to a first aspect of the present disclosure, a small molecule screening method targeting microRNA-mRNA interaction is provided, the method comprising:
[0005] Determine a first target nucleic acid sequence and a corresponding second target nucleic acid sequence, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA;
[0006] Determining a circular structure target sequence feature of a circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence, wherein the circular structure target sequence feature includes a potential feature and a statistical feature, wherein the potential feature is used to characterize the overall sequence feature of the circular structure target sequence, and the statistical feature is used to characterize the statistical feature of bases in the circular structure target sequence;
[0007] Obtaining molecular features corresponding to each small molecule in a small molecule database;
[0008] Determining a corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features, respectively, wherein the targeting probability value is used to characterize the affinity of the small molecule corresponding to the molecular feature targeting the ring structure target sequence;
[0009] Small molecule screening is performed according to the targeting probability value corresponding to each of the small molecules to obtain the target small molecules.
[0010] In a possible implementation, determining the first target nucleic acid sequence and the corresponding second target nucleic acid sequence includes:
[0011] Displaying an information acquisition interface through a display device, wherein the information acquisition interface includes a sequence acquisition control;
[0012] In response to the user triggering the sequence acquisition control by inputting the first target nucleic acid sequence and the second target nucleic acid sequence, the first target nucleic acid sequence and the second target nucleic acid sequence are acquired.
[0013] In a possible implementation, the determining the circular structure target sequence feature of the circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence includes:
[0014] Determining a target sequence of a circular structure formed by the first target nucleic acid sequence and the second target nucleic acid sequence;
[0015] Digitally encoding the ring structure target sequence through an autoencoder to obtain corresponding potential features;
[0016] By counting the bases in the target sequence of the circular structure, the corresponding statistical features are determined.
[0017] The corresponding circular structure target sequence features are determined according to the potential features and statistical features of the circular structure target.
[0018] In a possible implementation, the statistical features include at least one of sequence length, the number of each base, the proportion of the target base in all bases, and adjacent base pairs.
[0019] In a possible implementation, obtaining the molecular features corresponding to each small molecule in the small molecule database includes:
[0020] Obtaining molecular fingerprint features and molecular descriptor features corresponding to each small molecule in a small molecule database;
[0021] The corresponding molecular features are determined according to the molecular fingerprint features and the molecular descriptor features of each of the small molecules.
[0022] In a possible implementation, determining the corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features respectively includes:
[0023] Each of the molecular features is input into the trained convolutional neural network together with the ring structure target sequence feature, and the corresponding targeting probability value is output.
[0024] In a possible implementation, the small molecule screening is performed according to the targeting probability value corresponding to each of the small molecules to obtain the target small molecule, including:
[0025] Acquire small molecules whose targeting probability values are greater than a targeting probability value threshold as candidate small molecules;
[0026] In response to the number of the candidate small molecules being less than a preset number, determining the candidate small molecules to be target small molecules;
[0027] In response to the number of the candidate small molecules being greater than a preset number, a preset number of candidate small molecules with maximum targeting probability values are obtained as target small molecules.
[0028] In a possible implementation, the method further includes:
[0029] Determining basic information corresponding to each of the target small molecules, and comparative information obtained by comparing each of the target small molecules;
[0030] The targeting probability value and basic information corresponding to each target small molecule, as well as the comparison information, are displayed by a display device.
[0031] According to a second aspect of the present disclosure, a small molecule screening device targeting microRNA-mRNA interaction is provided, the device comprising:
[0032] A nucleic acid sequence determination module, used to determine a first target nucleic acid sequence and a corresponding second target nucleic acid sequence, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA;
[0033] A first feature determination module, used to determine a circular structure target sequence feature of a circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence, wherein the circular structure target sequence feature includes a potential feature and a statistical feature, wherein the potential feature is used to characterize the overall sequence feature of the circular structure target sequence, and the statistical feature is used to characterize the statistical feature of bases in the circular structure target sequence;
[0034] A second feature determination module is used to obtain the molecular features corresponding to each small molecule in the small molecule database;
[0035] A targeting probability value determination module, used to determine the corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features, wherein the targeting probability value is used to characterize the affinity of the small molecule corresponding to the molecular feature targeting the ring structure target sequence;
[0036] The small molecule screening module is used to screen small molecules according to the targeting probability value corresponding to each small molecule to obtain target small molecules.
[0037] In a possible implementation, the nucleic acid sequence determination module is further used to:
[0038] Displaying an information acquisition interface through a display device, wherein the information acquisition interface includes a sequence acquisition control;
[0039] In response to the user triggering the sequence acquisition control by inputting the first target nucleic acid sequence and the second target nucleic acid sequence, the first target nucleic acid sequence and the second target nucleic acid sequence are acquired.
[0040] In a possible implementation manner, the first feature determination module is further configured to:
[0041] Determining a target sequence of a circular structure formed by the first target nucleic acid sequence and the second target nucleic acid sequence;
[0042] Digitally encoding the ring structure target sequence through an autoencoder to obtain corresponding potential features;
[0043] By counting the bases in the target sequence of the circular structure, the corresponding statistical features are determined.
[0044] The corresponding circular structure target sequence features are determined according to the potential features and statistical features of the circular structure target.
[0045] In a possible implementation, the statistical features include at least one of sequence length, the number of each base, the proportion of the target base in all bases, and adjacent base pairs.
[0046] In a possible implementation manner, the second feature determination module is further configured to:
[0047] Obtaining molecular fingerprint features and molecular descriptor features corresponding to each small molecule in a small molecule database;
[0048] The corresponding molecular features are determined according to the molecular fingerprint features and the molecular descriptor features of each of the small molecules.
[0049] In a possible implementation, the targeting probability value determination module is further used to:
[0050] Each of the molecular features is input into the trained convolutional neural network together with the ring structure target sequence feature, and the corresponding targeting probability value is output.
[0051] In a possible implementation, the small molecule screening module is further used to:
[0052] Acquire small molecules whose targeting probability values are greater than a targeting probability value threshold as candidate small molecules;
[0053] In response to the number of the candidate small molecules being less than a preset number, determining the candidate small molecules to be target small molecules;
[0054] In response to the number of the candidate small molecules being greater than a preset number, a preset number of candidate small molecules with maximum targeting probability values are obtained as target small molecules.
[0055] In a possible implementation manner, the device further includes:
[0056] An information determination module, used to determine basic information corresponding to each of the target small molecules, and comparative information obtained by comparing each of the target small molecules;
[0057] The information display module is used to display the targeting probability value and basic information corresponding to each target small molecule and the comparison information through a display device.
[0058] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.
[0059] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.
[0060] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.
[0061] In an embodiment of the present disclosure, a first target nucleic acid sequence and a corresponding second target nucleic acid sequence are determined, the first target nucleic acid is mRNA, and the second target nucleic acid is microRNA. The potential characteristics and statistical characteristics of the ring structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence are determined, and the molecular characteristics corresponding to each small molecule in the small molecule database are obtained. The targeting probability value characterizing the affinity of the corresponding small molecule and the ring structure target sequence is determined according to the potential characteristics and statistical characteristics of each molecular feature and the ring structure target sequence. Small molecules are screened according to the corresponding targeting probability values to obtain target small molecules. The present disclosure can perform small molecule screening based on the characteristics of each small molecule and the characteristics of the ring structure target sequence, respectively. The screening method is simple and batch screening can be achieved, and the screening efficiency and accuracy of the screening results are high.
[0062] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0063] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
[0064] Figure 1 A flowchart showing a small molecule screening method targeting microRNA-mRNA interaction according to an embodiment of the present disclosure;
[0065] Figure 2 A schematic diagram showing a method for screening small molecules according to an embodiment of the present disclosure is shown;
[0066] Figure 3 A schematic diagram showing an information acquisition interface according to an embodiment of the present disclosure;
[0067] Figure 4 A schematic diagram of a convolutional neural network according to an embodiment of the present disclosure is shown;
[0068] Figure 5 A schematic diagram showing a method for determining a target small molecule according to an embodiment of the present disclosure is shown;
[0069] Figure 6 A schematic diagram showing basic information of a target small molecule according to an embodiment of the present disclosure;
[0070] Figure 7 A schematic diagram showing comparison information of a target small molecule according to an embodiment of the present disclosure;
[0071] Figure 8 A schematic diagram showing a small molecule screening device targeting microRNA-mRNA interaction according to an embodiment of the present disclosure;
[0072] Fig. 9 A schematic diagram showing an electronic device according to an embodiment of the present disclosure;
[0073] Fig.10 A schematic diagram showing another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0074] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.
[0075] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.
[0076] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.
[0077] The small molecule screening method for targeting microRNA-mRNA interaction of the disclosed embodiment can be performed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the small molecule screening method for targeting microRNA-mRNA interaction of the disclosed embodiment by a processor calling a computer-readable instruction stored in a memory.
[0078] Figure 1 A flowchart of a small molecule screening method targeting microRNA-mRNA interaction according to an embodiment of the present disclosure is shown. Figure 1 As shown, the small molecule screening method of the embodiment of the present disclosure may include the following steps S10-S50.
[0079] Step S10: Determine a first target nucleic acid sequence and a corresponding second target nucleic acid sequence.
[0080] In a possible implementation, the electronic device determines a first target nucleic acid and a second target nucleic acid that can interact with each other, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA, and in the case of interaction, the first target nucleic acid can bind to the second target nucleic acid by base pairing. The electronic device determines the first target nucleic acid sequence and the second target nucleic acid sequence while determining the first target nucleic acid and the second target nucleic acid. For example, the first target nucleic acid can be directly determined by the first target nucleic acid sequence, and the second target nucleic acid can be determined by the second target nucleic acid sequence.
[0081] Figure 2 A schematic diagram of screening small molecules according to an embodiment of the present disclosure is shown. Figure 2As shown, the first target nucleic acid and the second target nucleic acid interact with each other through complementary base pairing, wherein the part that fails to form a pairing will form a ring structure, i.e., a target for small molecule targeting. The principle of complementary base pairing is that base A (adenine) and base U (uracil) are connected to each other by hydrogen bonds, and base G (guanine) and base C (cytosine) are connected to each other by hydrogen bonds. Where the corresponding bases of the first target nucleic acid and the second target nucleic acid cannot be complementary paired, the double strands will separate to form a ring structure, which can be used as a target position for small molecule drug targeting.
[0082] Optionally, the electronic device can obtain the first target nucleic acid and the second target nucleic acid input by the user through human-computer interaction. Exemplarily, the electronic device can display an information acquisition interface through a display device, and the information acquisition interface includes a sequence acquisition control. In response to the user triggering the sequence acquisition control by inputting the first target nucleic acid sequence and the second target nucleic acid sequence, the first target nucleic acid sequence and the second target nucleic acid sequence are acquired. Among them, the sequence acquisition control may include a text input box and an information submission button, and the user can enter the first target nucleic acid sequence and the second target nucleic acid sequence in the text input box, and then trigger the sequence acquisition control by clicking the information submission button.
[0083] Figure 3 A schematic diagram of an information acquisition interface according to an embodiment of the present disclosure is shown. Figure 3 As shown, the information acquisition interface displayed by the electronic device includes a sequence acquisition control consisting of a text input box and an information submission button, wherein the text input box also includes a first sequence input box and a second sequence input box. The user can enter the first target nucleic acid sequence in the first sequence input box, enter the second target nucleic acid sequence in the second sequence input box, and trigger the sequence acquisition control by clicking the information submission button. When the sequence acquisition control is triggered, the electronic device acquires the first target nucleic acid sequence and the second target nucleic acid sequence received by the user.
[0084] Step S20: determining the circular structure target sequence characteristics of the circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence.
[0085] In a possible implementation, after determining the first target nucleic acid sequence and the second target nucleic acid sequence, the electronic device determines the position of the target that can form a circular structure based on the interaction and binding of the first target nucleic acid and the second target nucleic acid, and determines the corresponding circular structure target sequence. Optionally, the circular structure target sequence can be calculated based on the first target nucleic acid sequence and the second target nucleic acid sequence by a preset software, and the preset software can be RNA22. After determining the circular structure target sequence, the electronic device obtains the corresponding latent feature and statistical feature in two different dimensions to obtain the circular structure target sequence feature. Among them, the latent feature is used to characterize the overall sequence characteristics of the circular structure target sequence, and the statistical feature is used to characterize the statistical characteristics of the bases in the circular structure target sequence.
[0086] Optionally, the potential features of the circular structure target sequence can be realized by an autoencoder, and the statistical features can be determined by counting the bases in the circular structure target sequence. That is, the electronic device can first determine the circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence. The corresponding potential features are obtained by digitally encoding the circular structure target sequence through the autoencoder, and the corresponding statistical features are determined by counting the bases in the circular structure target sequence. Among them, the digital encoding process can convert the base string of the circular structure target sequence into the features of the digital string through the autoencoder, and the statistical features can include at least one of the sequence length, the number of each base, the proportion of the number of target bases in all bases, and adjacent base pairs. The sequence length is the number of bases included in the circular structure target, and the target base can be guanine and cytosine, that is, the proportion of the number of target bases in all bases can include the proportion of guanine and the proportion of cytosine, and the adjacent base pairs are used to record the bases adjacent to each two positions in the circular structure target sequence.
[0087] Step S30: Obtain the molecular features corresponding to each small molecule in the small molecule database.
[0088] In a possible implementation, the electronic device sequentially obtains each small molecule from a preset small molecule database and determines the molecular features corresponding to each small molecule. The small molecules included in the small molecule database are small molecules that need to be matched with the ring structure target. After determining the molecular features of each small molecule, the electronic device can judge the matching degree between the small molecule and the ring structure target based on the molecular features corresponding to each small molecule and the features of the ring structure target sequence.
[0089] Optionally, the molecular features of small molecules may include molecular fingerprint features and molecular descriptor features, that is, the electronic device determines the molecular features corresponding to each small molecule by obtaining the molecular fingerprint features and molecular descriptor features corresponding to each small molecule in the small molecule database, and determining the corresponding molecular features according to the molecular fingerprint features and molecular descriptor features of each small molecule. Among them, the molecular fingerprint features and molecular descriptor features are used to describe molecular features from different dimensions, and can be extracted by preset feature extraction software, and the feature extraction software can be RDKit. After obtaining the molecular fingerprint features and molecular descriptor features corresponding to the small molecule, the electronic device can merge the molecular fingerprint features and molecular descriptor features to obtain the molecular features corresponding to the small molecule.
[0090] Step S40: Determine a corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features.
[0091] In a possible implementation, after determining the potential features and statistical features corresponding to the ring structure target sequence, and the molecular features corresponding to each small molecule, the electronic device determines the targeting probability value corresponding to the small molecule according to the molecular features of each small molecule and the potential features and statistical features of the ring structure target sequence. Among them, the targeting probability value is used to characterize the affinity of the small molecule targeting the ring structure target sequence corresponding to the molecular features, that is, the degree of matching between the small molecule and the ring structure target. Optionally, the potential features and statistical features of the ring target sequence can be combined into a ring structure target sequence feature before predicting the targeting probability value, and the targeting probability value can be predicted based on the trained convolutional neural network. That is, the electronic device can first determine the ring structure target sequence feature based on the potential features and statistical features of the ring structure target sequence, and then input each molecular feature and the ring structure target sequence feature into the trained convolutional neural network, and output the corresponding targeting probability value.
[0092] Figure 4 FIG. 1 is a schematic diagram of a convolutional neural network according to an embodiment of the present disclosure. Figure 4 As shown, the convolutional neural network of the embodiment of the present disclosure may include an input layer, a multi-layer convolutional layer, a fully connected layer, and an output layer. The ring structure target sequence characteristics and molecular characteristics input to the convolutional neural network are processed by the above-mentioned multiple processing layers once and then the corresponding targeting probability values are output. During the training process, multiple sample data can be set according to a preset positive and negative sample ratio, including sample sequence characteristics of sample target sequences and sample molecular characteristics of sample small molecules. Each sample data can be input into the convolutional neural network for supervised training to obtain a trained convolutional neural network.
[0093] Step S50: performing small molecule screening according to the targeting probability value corresponding to each of the small molecules to obtain target small molecules.
[0094] In a possible implementation, after determining the targeting probability value corresponding to each small molecule, the electronic device judges the affinity of the small molecule and the ring structure target based on the targeting probability value corresponding to the small molecule, so as to screen and obtain the target small molecule that can be formed in the process of interacting with the first target nucleic acid and the second target nucleic acid. The target target is formed by the affinity of the ring structure target, thereby hindering the second target nucleic acid from destroying the stability of the first target nucleic acid or inhibiting the translation. Optionally, the target small molecule can be screened by at least one of a preset number and a preset targeting probability value threshold, for example, a small molecule with a targeting probability value greater than the targeting probability value threshold can be determined as a target small molecule, or a preset number of small molecules with the largest targeting probability value can be determined as a target small molecule. Alternatively, the electronic device can first obtain a small molecule with a targeting probability value greater than the targeting probability value threshold as a candidate small molecule, and in response to the number of candidate small molecules being less than the preset number, determine the candidate small molecule as a target small molecule. In response to the number of candidate small molecules being greater than the preset number, a preset number of candidate small molecules with the largest targeting probability value are obtained as target small molecules.
[0095] Further, the disclosed embodiment can also intuitively display the target small molecule and at least one corresponding information to the user through a display device after obtaining the target small molecule. Among them, the electronic device can determine the basic information corresponding to each of the target small molecules, and the comparative information obtained by comparing each target small molecule, and then display the targeting probability value and basic information corresponding to each target small molecule through a display device, as well as the comparative information. Optionally, the basic information may include the targeting probability value corresponding to the target small molecule, a molecular structure diagram, a simplified molecular linear input specification (SMILES), an international chemical identifier (InChIKey), and a sorting number, etc. The comparative information can be a similarity comparison matrix obtained by comparing the similarities between multiple target small molecules.
[0096] Figure 5 A schematic diagram of determining a target small molecule according to an embodiment of the present disclosure is shown. Figure 5As shown, the disclosed embodiment can determine the first target nucleic acid sequence and the corresponding second target nucleic acid sequence by human-computer interaction, and calculate the corresponding ring structure target sequence according to the first target nucleic acid sequence and the second target nucleic acid sequence, and determine the potential characteristics and statistical characteristics of the ring structure target sequence. At the same time, the electronic device also obtains the molecular features corresponding to each small molecule in the small molecule database. The convolutional neural network is used to determine the targeting probability value that characterizes the affinity of the corresponding small molecule and the ring structure target sequence according to the potential characteristics and statistical characteristics of each molecular feature and the ring structure target sequence. Small molecules are screened according to the corresponding targeting probability values to obtain target small molecules, and the target small molecules and corresponding basic information are further displayed through a display device.
[0097] Figure 6 A schematic diagram showing basic information of a target small molecule according to an embodiment of the present disclosure. Figure 6 As shown, the basic information of the target small molecules of the disclosed embodiment may include the target probability value, molecular structure diagram, simplified molecular linear input specification (SMILES), international chemical identifier (InChIKey) and sorting number corresponding to each target small molecule.
[0098] Figure 7 A schematic diagram showing comparison information of a target small molecule according to an embodiment of the present disclosure is shown. Figure 7 As described above, the comparison information of the target small molecules in the embodiment of the present disclosure may include a similarity comparison matrix obtained by comparing the similarities between multiple target small molecules.
[0099] Based on the above technical features, the embodiments of the present disclosure can perform small molecule screening based on the characteristics of each small molecule and the characteristics of the ring structure target sequence respectively. The screening method and the required input RNA sequence form are simple and batch screening can be achieved, and the screening efficiency and accuracy of the screening results are high.
[0100] Figure 8 A schematic diagram of a small molecule screening device targeting microRNA-mRNA interaction according to an embodiment of the present disclosure is shown. Figure 8 The small molecule screening device of the embodiment of the present disclosure may include:
[0101] A nucleic acid sequence determination module 80, for determining a first target nucleic acid sequence and a corresponding second target nucleic acid sequence, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA;
[0102] A first feature determination module 81 is used to determine a circular structure target sequence feature of a circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence, wherein the circular structure target sequence feature includes a potential feature and a statistical feature, wherein the potential feature is used to characterize the overall sequence feature of the circular structure target sequence, and the statistical feature is used to characterize the statistical feature of bases in the circular structure target sequence;
[0103] A second feature determination module 82 is used to obtain the molecular features corresponding to each small molecule in the small molecule database;
[0104] A targeting probability value determination module 83 is used to determine a corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features, wherein the targeting probability value is used to characterize the affinity of the small molecule corresponding to the molecular feature targeting the ring structure target sequence;
[0105] The small molecule screening module 84 is used to screen small molecules according to the targeting probability value corresponding to each small molecule to obtain target small molecules.
[0106] In a possible implementation, the nucleic acid sequence determination module 80 is further used to:
[0107] Displaying an information acquisition interface through a display device, wherein the information acquisition interface includes a sequence acquisition control;
[0108] In response to the user triggering the sequence acquisition control by inputting the first target nucleic acid sequence and the second target nucleic acid sequence, the first target nucleic acid sequence and the second target nucleic acid sequence are acquired.
[0109] In a possible implementation, the first feature determination module 81 is further configured to:
[0110] Determining a target sequence of a circular structure formed by the first target nucleic acid sequence and the second target nucleic acid sequence;
[0111] Digitally encoding the ring structure target sequence through an autoencoder to obtain corresponding potential features;
[0112] By counting the bases in the target sequence of the circular structure, the corresponding statistical features are determined.
[0113] The corresponding circular structure target sequence features are determined according to the potential features and statistical features of the circular structure target.
[0114] In a possible implementation, the statistical features include at least one of sequence length, the number of each base, the proportion of the target base in all bases, and adjacent base pairs.
[0115] In a possible implementation, the second feature determination module 82 is further configured to:
[0116] Obtaining molecular fingerprint features and molecular descriptor features corresponding to each small molecule in a small molecule database;
[0117] The corresponding molecular features are determined according to the molecular fingerprint features and the molecular descriptor features of each of the small molecules.
[0118] In a possible implementation, the targeting probability value determination module 83 is further configured to:
[0119] Each of the molecular features is input into the trained convolutional neural network together with the ring structure target sequence feature, and the corresponding targeting probability value is output.
[0120] In a possible implementation, the small molecule screening module 84 is further used to:
[0121] Acquire small molecules whose targeting probability values are greater than a targeting probability value threshold as candidate small molecules;
[0122] In response to the number of the candidate small molecules being less than a preset number, determining the candidate small molecules to be target small molecules;
[0123] In response to the number of the candidate small molecules being greater than a preset number, a preset number of candidate small molecules with maximum targeting probability values are obtained as target small molecules.
[0124] In a possible implementation manner, the device further includes:
[0125] An information determination module, used to determine basic information corresponding to each of the target small molecules, and comparative information obtained by comparing each of the target small molecules;
[0126] The information display module is used to display the targeting probability value and basic information corresponding to each target small molecule and the comparison information through a display device.
[0127] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.
[0128] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.
[0129] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.
[0130] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.
[0131] Fig. 9 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
[0132] Reference Fig. 9 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .
[0133] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
[0134] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
[0135] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
[0136] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
[0137] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.
[0138] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.
[0139] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
[0140] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
[0141] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.
[0142] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.
[0143] Fig.10 1 is a schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Fig.10, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.
[0144] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.
[0145] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.
[0146] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.
[0147] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.
[0148] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.
[0149] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.
[0150] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.
[0151] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0152] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0153] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.
[0154] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.
Claims
1. A small molecule screening method targeting microRNA-mRNA interaction, characterized in that: The method comprises: Determine a first target nucleic acid sequence and a corresponding second target nucleic acid sequence, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA; Determining a circular structure target sequence feature of a circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence, wherein the circular structure target sequence feature includes a potential feature and a statistical feature, wherein the potential feature is used to characterize the overall sequence feature of the circular structure target sequence, and the statistical feature is used to characterize the statistical feature of bases in the circular structure target sequence; Obtaining molecular features corresponding to each small molecule in a small molecule database; Determining a corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features, respectively, wherein the targeting probability value is used to characterize the affinity of the small molecule corresponding to the molecular feature targeting the ring structure target sequence; Small molecule screening is performed according to the targeting probability value corresponding to each small molecule to obtain the target small molecule.
2. The method according to claim 1, characterized in that The determining of the first target nucleic acid sequence and the corresponding second target nucleic acid sequence comprises: Displaying an information acquisition interface through a display device, wherein the information acquisition interface includes a sequence acquisition control; In response to the user triggering the sequence acquisition control by inputting the first target nucleic acid sequence and the second target nucleic acid sequence, the first target nucleic acid sequence and the second target nucleic acid sequence are acquired.
3. The method according to claim 1 or 2, characterized in that: The step of determining the circular structure target sequence feature of the circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence comprises: Determining a target sequence of a circular structure formed by the first target nucleic acid sequence and the second target nucleic acid sequence; Digitally encoding the ring structure target sequence through an autoencoder to obtain corresponding potential features; Determine corresponding statistical features by counting the bases in the target sequence of the circular structure; The corresponding circular structure target sequence features are determined according to the potential features and statistical features of the circular structure target.
4. The method according to claim 3, characterized in that The statistical features include at least one of sequence length, the number of each base, the proportion of the target base in all bases, and adjacent base pairs.
5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the molecular features corresponding to each small molecule in the small molecule database comprises: Obtaining molecular fingerprint features and molecular descriptor features corresponding to each small molecule in a small molecule database; The corresponding molecular features are determined according to the molecular fingerprint features and the molecular descriptor features of each of the small molecules.
6. The method according to any one of claims 1 to 5, characterized in that: Determining the corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features respectively includes: Each of the molecular features is input into the trained convolutional neural network together with the ring structure target sequence feature, and the corresponding targeting probability value is output.
7. The method according to any one of claims 1 to 6, characterized in that: The small molecule screening is performed according to the targeting probability value corresponding to each small molecule to obtain the target small molecule, including: Acquire small molecules whose targeting probability values are greater than a targeting probability value threshold as candidate small molecules; In response to the number of the candidate small molecules being less than a preset number, determining the candidate small molecules to be target small molecules; In response to the number of the candidate small molecules being greater than a preset number, a preset number of candidate small molecules with maximum targeting probability values are obtained as target small molecules.
8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: Determining basic information corresponding to each of the target small molecules, and comparative information obtained by comparing each of the target small molecules; The targeting probability value and basic information corresponding to each target small molecule, as well as the comparison information, are displayed by a display device.
9. A small molecule screening device targeting microRNA-mRNA interaction, characterized in that: The device comprises: A nucleic acid sequence determination module, used to determine a first target nucleic acid sequence and a corresponding second target nucleic acid sequence, wherein the first target nucleic acid is mRNA and the second target nucleic acid is microRNA; A first feature determination module, used to determine a circular structure target sequence feature of a circular structure target sequence formed by the first target nucleic acid sequence and the second target nucleic acid sequence, wherein the circular structure target sequence feature includes a potential feature and a statistical feature, wherein the potential feature is used to characterize the overall sequence feature of the circular structure target sequence, and the statistical feature is used to characterize the statistical feature of bases in the circular structure target sequence; A second feature determination module is used to obtain the molecular features corresponding to each small molecule in the small molecule database; A targeting probability value determination module, used to determine the corresponding targeting probability value according to each of the molecular features and the ring structure target sequence features, wherein the targeting probability value is used to characterize the affinity of the small molecule corresponding to the molecular feature targeting the ring structure target sequence; The small molecule screening module is used to screen small molecules according to the targeting probability value corresponding to each small molecule to obtain target small molecules.
10. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 8 when executing the instructions stored in the memory.
11. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.