CN116646029B - Quantum approximation optimization algorithm-based molecular docking method, device, medium and equipment - Google Patents
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Abstract
The application discloses a molecular docking method, a device, a medium and equipment based on a quantum approximation optimization algorithm, wherein a binding site distance graph of a protein receptor and a drug ligand is calculated according to a binding site by acquiring the binding site of the protein receptor and the drug ligand, a binding interaction graph of the protein receptor and the drug ligand is constructed, and a maximum weighted graph of the binding interaction graph is solved by adopting the quantum approximation optimization algorithm to obtain a docking result of the protein receptor and the drug ligand; the method comprises the steps of calculating a corresponding binding site distance graph aiming at binding sites of a protein receptor and a drug ligand, constructing a binding interaction graph based on the binding site distance graph, solving a maximum weight graph of the binding interaction graph with weight through a quantum approximation optimization algorithm to obtain the binding sites and the binding postures of the protein receptor and the drug ligand, and accelerating solving through the quantum approximation optimization algorithm to obtain a molecular docking result, so that the calculation accuracy is guaranteed to obtain a global optimal solution, and the calculation speed is improved.
Description
Technical Field
The application relates to the technical field of molecular docking, in particular to a molecular docking method, device, medium and equipment based on a quantum approximation optimization algorithm.
Background
The research and development of new drugs is a complex system engineering, and after the lead compounds are found through large-scale screening, repeated in-vitro experiments, animal experiments and clinical experiments are needed for testing and optimizing. One innovative drug costs billions of dollars and 10-15 years from development to last market. If a drug is developed for a specific type of disease (i.e., precise medical treatment), huge manpower and material resources are required. In order to improve the drug development efficiency, shorten the development period, save the development cost, and particularly important to utilize a reasonable drug design method, wherein the drug discovery hit rate can be effectively improved by utilizing Computer Aided Drug Design (CADD) to screen drugs, and the method has become an indispensable part in the drug discovery process.
In recent years, with the development of powerful molecular modeling tools and the increase of the number of protein small molecule complex analytical structures, structure-based drug design is an indispensable tool in new drug development. The most widely used protein-ligand binding conformational computational tool at present is molecular docking. Molecular docking is a theoretical simulation method by which interactions between ligands (meaning various small molecules capable of binding to proteins, including metal ions, cofactors, substrates, inhibitors or agonist molecules, etc.) and receptors (e.g., proteins or other biological macromolecules) are studied and their binding patterns and affinities predicted. The aim is to determine the binding pattern between molecules to predict the strength and affinity of interactions between them, to find potential drug molecules or to optimise existing drug molecules. Molecular docking can help researchers screen out the most likely drug molecules from a large number of small molecule libraries, thereby improving the efficiency of drug development.
In molecular docking processes, proteins and small molecules are generally considered rigid molecules and assume that their position and orientation in space are fixed. The small molecules are then searched for potential binding patterns at the active site of the protein using computational methods, and these binding patterns are scored to determine the most likely binding pattern. Scoring methods are typically based on molecular or statistical mechanics principles, such as energy minimization or partitioning function analysis.
Existing molecular docking methods generally assume that the conformation of the molecule in space is rigid. This assumption may not be true in certain situations, for example, where a protein molecule may undergo a conformational change when bound to a ligand. Thus, traditional site selection may not accurately predict intermolecular interactions. Moreover, existing molecular docking methods require searching a large compound space to find the ligand that best matches the target protein. Such search spaces can be very large, requiring significant time and computing resources. Furthermore, the size of the search space may lead to omission of potential ligands.
In summary, the existing molecular docking method has limitations, which may affect the accuracy and reliability of molecular docking.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides a molecular docking method, device, medium and equipment based on a quantum approximation optimization algorithm, which solve the technical problems.
According to one aspect of the present application, there is provided a quantum approximation optimization algorithm-based molecular docking method, including: obtaining a binding site for a protein receptor and a drug ligand; calculating a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites; constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand; the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and solving a maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain a butt joint result of the protein receptor and the drug ligand.
In one embodiment, the constructing a binding interaction diagram of the protein receptor and the drug ligand based on the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand comprises: establishing an initial interaction diagram; wherein the number of vertices of the initial interaction graph is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand; and determining the connection state of each pair of vertexes in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand so as to obtain the binding interaction diagram.
In one embodiment, the determining the connection state of each pair of vertices in the initial interaction graph according to the binding site distance graph of the protein receptor and the binding site distance graph of the drug ligand to obtain the binding interaction graph includes: determining the connection state of each pair of vertices in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand; and setting corresponding weights according to potential energy of each vertex in the initial interaction diagram to obtain the binding interaction diagram.
In an embodiment, after the maximum weighted graph of the binding interaction graph is solved by adopting a quantum approximation optimization algorithm to obtain a docking result of the protein receptor and the drug ligand, the molecular docking method based on the quantum approximation optimization algorithm further comprises: and verifying and evaluating the butt joint result.
In one embodiment, the acquiring the binding site for the protein receptor and the drug ligand comprises: the binding sites for the protein receptor and the drug ligand are selected by a protein ligand complex, or experimental data, or software package.
In one embodiment, the quantum-based approximate optimization algorithm molecular docking method further comprises, prior to the acquiring the binding sites of the protein receptor and the drug ligand: selecting the drug ligand from drug molecules to be screened; wherein the structure of the drug ligand is obtained by an experimental method.
In one embodiment, the quantum-based approximate optimization algorithm molecular docking method further comprises, prior to the acquiring the binding sites of the protein receptor and the drug ligand: selecting said protein receptor from a receptor library; wherein the structure of the protein receptor is obtained by experimental methods.
According to another aspect of the present application, there is provided a quantum approximation optimization algorithm-based molecular docking apparatus, comprising: a site acquisition module for acquiring binding sites for a protein receptor and a drug ligand; a distance calculation module for calculating a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites; an interactive binding module for constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand; the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and a result solving module for solving the maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain the butt joint result of the protein receptor and the drug ligand.
According to another aspect of the present application, there is provided a computer readable storage medium storing a computer program for performing any one of the above-described quantum-based approximate optimization algorithm molecular docking methods.
According to another aspect of the present application, there is provided an electronic device including: a processor; a memory for storing the processor-executable instructions; the processor is used for executing the molecular docking method based on the quantum approximation optimization algorithm.
The molecular docking method, the device, the medium and the equipment based on the quantum approximation optimization algorithm provided by the application are characterized in that the binding sites of a protein receptor and a drug ligand are obtained; calculating binding site distance maps of the protein receptor and the drug ligand respectively according to the binding sites; constructing a binding interaction diagram of the protein receptor and the drug ligand based on the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand; wherein the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; solving a maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain a butt joint result of the protein receptor and the drug ligand; the method comprises the steps of calculating a corresponding binding site distance graph aiming at binding sites of a protein receptor and a drug ligand, constructing a binding interaction graph based on the binding site distance graph, solving a maximum weight graph of the binding interaction graph with weight through a quantum approximation optimization algorithm to obtain the binding sites and the binding postures of the protein receptor and the drug ligand, and accelerating solving through the quantum approximation optimization algorithm to obtain a molecular docking result, so that the calculation accuracy is guaranteed to obtain a global optimal solution, and the calculation speed is improved.
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The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to an exemplary embodiment of the present application.
Fig. 2 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application.
Fig. 3 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application.
Fig. 4 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application.
Fig. 5 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application.
Fig. 6 is a schematic structural diagram of a molecular docking device based on a quantum approximation optimization algorithm according to an exemplary embodiment of the present application.
Fig. 7 is a schematic structural diagram of a molecular docking device based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application.
Fig. 8 is a block diagram of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Molecular docking first creates a pool of spheres that fills the pockets or grooves in the surface of the receptor molecule, and then creates a series of putative binding sites. According to the principle of distance matching of these binding sites on the receptor surface to the ligand molecules, the ligand molecules are projected onto the receptor molecule surface and a set of possible receptor-ligand binding conformations is obtained by rotating and twisting the ligand molecules. Then, the affinity of these binding conformations is calculated, the calculation result is scored, the optimal receptor ligand binding conformation is determined according to the score and the calculation of the binding capacity of the ligand to the receptor is given based on the conformation.
There are two key problems in the molecular docking process: one is a binding site and the other is a binding posture. Since the binding of the ligand to the protein results mainly from hydrogen bonding, ionic bonding, electrostatic interactions and hydrophobic interactions formed between the protein atom and the ligand atom, covalent bonds are sometimes included. Molecular docking is based on the "lock-key principle" (lock and key principle) of ligand-receptor interactions that mimic interactions between small molecule ligands and receptor biological macromolecules, which mainly include electrostatic interactions, hydrogen bonding, hydrophobic interactions, van der Waals interactions, and the like.
For the selection of the combined gesture, a Scoring Function (Scoring Function) may be used. The method converts the selection of binding poses into an optimization problem and evaluates the binding capacity of different ligands by a scoring function. Scoring functions typically consist of a weighted sum of various physical and chemical factors, including intermolecular van der Waals forces, electrostatic forces, hydrogen bonding, and the like. The method has the advantages that the method is suitable for the problem of large-scale molecular docking, and factors such as flexibility and water solubility of molecules can be considered. The disadvantage is that the accuracy and reliability of the scoring function is largely dependent on the choice of weights, and high accuracy is difficult to achieve for complex molecular docking problems. In the scoring function method, most of screening modes of the traditional model adopt heuristic algorithms and system searching algorithms, so that the time consumption is long, and the optimal solution may not be calculated. For example, simulated annealing (Simulated Annealing): the method searches for an optimal solution in energy space by simulating the annealing process of solid matter. In the simulation process, the conformation of the ligand molecule is regarded as a 'higher temperature' state, and as the simulation process progresses, the temperature gradually decreases and the conformation gradually stabilizes. The method has the advantages of being capable of treating factors such as flexibility and water solubility of molecules, and being suitable for the problem of butt joint of different types of molecules. The disadvantage is that a large number of calculations are required and the convergence speed is slow. In addition, conventional models require a large number of samples to achieve a lower energy constellation, and this constellation may not be globally optimal.
The implementation modes of the molecular docking method, the device, the medium and the electronic equipment based on the quantum approximation optimization algorithm provided by the application are specifically described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to an exemplary embodiment of the present application. As shown in fig. 1, the molecular docking method based on the quantum approximation optimization algorithm comprises the following steps:
step 110: the binding sites for the protein receptor and the drug ligand are obtained.
Molecular docking means that the structures of two molecules are matched to each other to predict the interaction between them. The purpose of molecular docking is to determine the manner of binding between molecules to predict the strength and affinity of interactions between them, to find potential drug molecules or to optimize existing drug molecules.
In one embodiment, the specific implementation of step 110 may be: the binding sites for the protein receptor and the drug ligand are selected by the protein ligand complex, or experimental data, or software package. Specifically, if the experimentally measured structure is available, the binding site is selected by the protein ligand complex, otherwise the binding site can be predicted by means of available experimental data or specific software packages. In obtaining binding sites for protein receptors and drug ligands, careful selection of the size of the binding sites is required to ensure the efficiency and outcome of the virtual screening procedure, since potential ligands unsuitable for use therein will be discarded if the defined binding sites are too small, and if the defined binding sites are too large, much computation time will be used to search for regions of no interest.
Step 120: based on the binding sites, a binding site distance map of the protein receptor and the drug ligand is calculated, respectively.
After the binding sites are selected, a distance map in three-dimensional space of the binding sites is calculated on the protein receptor and the drug ligand, respectively, to obtain a binding site distance map of the protein receptor and a binding site distance map of the drug ligand.
Step 130: a binding interaction diagram of the protein receptor and the drug ligand is constructed based on the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand.
Wherein the number of vertices of the binding interaction graph is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weights are set at each vertex of the binding interaction graph. Binding two point connections on the interaction graph means that the paired gestures coexist, i.e. one binding site and a binding gesture. The application shows the docking result of the protein receptor and the drug ligand through a binding interaction diagram of the protein receptor and the drug ligand.
Step 140: and solving a maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain a butt joint result of the protein receptor and the drug ligand.
Specifically, the docking result of the protein receptor and the drug ligand can be obtained by adopting a QAOA algorithm. The quantum computing is a novel computing mode for regulating and controlling a quantum information unit to perform computing according to a quantum mechanics rule, namely, the quantum computing is realized by taking quantum bits formed by microscopic particles as basic units and by controlled evolution of quantum states by utilizing physical characteristics such as quantum superposition and entanglement. Compared with the traditional computer, the quantum computer can realize exponential scale expansion and explosive growth of calculation power and presents quantum superiority. In recent years, there has been a great deal of development in both hardware and algorithms, making quantum computers closer to their upcoming commercial uses, such as biopharmaceuticals, traffic logistics, finance, and the like. In particular, limited by the computational power limits of classical computers, it appears to be very difficult to accurately and rapidly model drugs, i.e., to accurately and efficiently understand and quantify interactions of candidate drugs with multiple biological targets. In ideal cases, the efficient and accurate calculation of the scoring function of the combination of the drug and the target point is performed through the calculation from the head calculation quantum mechanics, and the calculation complexity is exponentially increased along with the electron number. However, with quantum computers, the scoring function can be calculated more efficiently and accurately.
The quantum approximation optimization algorithm (Quantum Approximate Optimization Algorithm, QAOA for short) is a hybrid algorithm, and combines classical and quantum computing to solve the combination optimization problem. The QAOA algorithm comprises three main steps: the first step is to encode the optimization problem into Ha Midu, the hamiltonian is a mathematical representation of the quantum system energy; the hamiltonian is designed such that the lowest energy state of the system corresponds to the optimal solution to the optimization problem. The second step is to prepare a quantum state into which a candidate solution is encoded by applying a series of quantum gates to an initial state, such as the superposition of all possible solutions; the gates used in QAOA are typically Pauli-X and Pauli-Z gates, which are simple operations that can be easily implemented on quantum hardware. The final step is to measure the quantum states and obtain an approximation of the optimal solution, by making multiple measurements of the states and extracting the most likely solution using classical post-processing, the number of measurements required to obtain a good approximation depending on the complexity of the problem and the required accuracy.
According to the molecular docking method based on the quantum approximation optimization algorithm, binding sites of a protein receptor and a drug ligand are obtained; calculating binding site distance maps of the protein receptor and the drug ligand respectively according to the binding sites; constructing a binding interaction diagram of the protein receptor and the drug ligand based on the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand; wherein the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; solving a maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain a butt joint result of the protein receptor and the drug ligand; the method comprises the steps of calculating a corresponding binding site distance graph aiming at binding sites of a protein receptor and a drug ligand, constructing a binding interaction graph based on the binding site distance graph, solving a maximum weight graph of the binding interaction graph with weight through a quantum approximation optimization algorithm to obtain the binding sites and the binding postures of the protein receptor and the drug ligand, and accelerating solving through the quantum approximation optimization algorithm to obtain a molecular docking result, so that the calculation accuracy is guaranteed to obtain a global optimal solution, and the calculation speed is improved.
Fig. 2 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application. As shown in fig. 2, the step 130 may include:
step 131: an initial interaction diagram is established.
Wherein the number of vertices of the initial interaction pattern is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand. The initial interaction graph holds for any binding, i.e., any pair of vertices may be connected.
Step 132: and determining the connection state of each pair of vertexes in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand so as to obtain a binding interaction diagram.
In one embodiment, the specific implementation of step 132 may be: and determining the connection state of each pair of vertexes in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand, and setting corresponding weights according to the potential energy of each vertex in the initial interaction diagram to obtain the binding interaction diagram. Judging whether any pair of vertexes can be communicated one by one according to the three-dimensional distance of the binding site, thereby obtaining the possible binding mode of the protein receptor and the drug ligand.
Fig. 3 is a flow chart of a docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application. As shown in fig. 3, after step 140, the quantum-based approximate optimization algorithm molecular docking method may further include:
step 150: and verifying and evaluating the butt joint result.
After solving for the result of the docking of the protein receptor and the drug ligand, the result is validated and evaluated, i.e., the structure and interaction of the protein receptor and the drug ligand is validated and evaluated, specifically by experimental methods such as biophysics, biochemistry, and the like.
Fig. 4 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application. As shown in fig. 4, before step 110, the molecular docking method based on the quantum approximation optimization algorithm may further include:
step 160: selecting a drug ligand from drug molecules to be screened.
Wherein the structure of the drug ligand is obtained by an experimental method. Specifically, the drug molecules are selected from a drug molecule library to be screened, and the screening principle can include: hydrogen bonds donor (OH, NH) no more than 5; hydrogen bond acceptors (N, O, etc.) are not more than 10; the lipid water partition coefficient (LogP) is not more than 5; the molecular weight is below 500; the number of rotatable keys is not more than 10. The structure of the drug ligand may be obtained by experimental methods such as NMR and the like, or by computational methods such as molecular dynamics simulation, quantum chemistry calculation and the like.
Fig. 5 is a flow chart of a molecular docking method based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application. As shown in fig. 5, before step 110, the molecular docking method based on the quantum approximation optimization algorithm may further include:
step 170: protein receptors are selected from a receptor library.
Wherein the structure of the protein receptor is obtained by experimental methods. Specifically, the reactive protein is selected from a receptor library, and the structure of the protein receptor can be obtained by experimental methods such as X-ray crystallography, nuclear magnetic resonance, and the like.
Fig. 6 is a schematic structural diagram of a molecular docking device based on a quantum approximation optimization algorithm according to an exemplary embodiment of the present application. As shown in fig. 6, the quantum-based approximate optimization algorithm molecular docking device 60 includes: a site acquisition module 61 for acquiring binding sites of a protein receptor and a drug ligand; a distance calculation module 62 for calculating a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites; an interaction binding module 63 for constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand; wherein the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and a result solving module 64, configured to solve the maximum weighted graph of the binding interaction graph by using a quantum approximation optimization algorithm, so as to obtain a docking result of the protein receptor and the drug ligand.
The molecular docking device based on the quantum approximation optimization algorithm acquires the binding sites of a protein receptor and a drug ligand through a site acquisition module 61; the distance calculation module 62 calculates a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites; the interaction binding module 63 constructs a binding interaction diagram of the protein receptor and the drug ligand based on the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand; wherein the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and the result solving module 64 adopts a quantum approximation optimization algorithm to solve the maximum weighted graph of the binding interaction graph, so as to obtain the butting result of the protein receptor and the drug ligand; the method comprises the steps of calculating a corresponding binding site distance graph aiming at binding sites of a protein receptor and a drug ligand, constructing a binding interaction graph based on the binding site distance graph, solving a maximum weight graph of the binding interaction graph with weight through a quantum approximation optimization algorithm to obtain the binding sites and the binding postures of the protein receptor and the drug ligand, and accelerating solving through the quantum approximation optimization algorithm to obtain a molecular docking result, so that the calculation accuracy is guaranteed to obtain a global optimal solution, and the calculation speed is improved.
In an embodiment, the above-mentioned site acquisition module 61 may be further configured to: the binding sites for the protein receptor and the drug ligand are selected by the protein ligand complex, or experimental data, or software package.
Fig. 7 is a schematic structural diagram of a molecular docking device based on a quantum approximation optimization algorithm according to another exemplary embodiment of the present application. As shown in fig. 7, the interactive binding module 63 may include: a graph creation unit 631 for creating an initial interaction graph in which the number of vertices of the initial interaction graph is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand; and a vertex connection unit 632 for determining the connection state of each pair of vertices in the initial interaction graph according to the binding site distance graph of the protein receptor and the binding site distance graph of the drug ligand, so as to obtain a binding interaction graph.
In an embodiment, vertex connection unit 632 may be further configured to: and determining the connection state of each pair of vertexes in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand, and setting corresponding weights according to the potential energy of each vertex in the initial interaction diagram to obtain the binding interaction diagram.
In one embodiment, as shown in fig. 7, the molecular docking device 60 based on the quantum approximation optimization algorithm may further include: the result evaluation module 65 is configured to verify and evaluate the docking result.
In one embodiment, as shown in fig. 7, the molecular docking device 60 based on the quantum approximation optimization algorithm may further include: the ligand screening module 66 is configured to select a drug ligand from the drug molecules to be screened, where the structure of the drug ligand is obtained through an experimental method.
In one embodiment, as shown in fig. 7, the molecular docking device 60 based on the quantum approximation optimization algorithm may further include: the receptor selection module 67 is configured to select a protein receptor from a receptor library, where the structure of the protein receptor is obtained through an experimental method.
Next, an electronic device according to an embodiment of the present application is described with reference to fig. 8. The electronic device may be either or both of the first device and the second device, or a stand-alone device independent thereof, which may communicate with the first device and the second device to receive the acquired input signals therefrom.
Fig. 8 illustrates a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 8, the electronic device 10 includes one or more processors 11 and a memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 11 to implement the quantum approximation optimization algorithm-based molecular docking method of the various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information to the outside, including the determined distance information, direction information, and the like. The output device 14 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 10 that are relevant to the present application are shown in fig. 8 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in a quantum approximation optimization algorithm-based molecular docking method according to various embodiments of the present application described in the "exemplary methods" section of the present specification.
The computer program product may write program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform the steps in a quantum approximation optimization algorithm-based molecular docking method according to various embodiments of the present application described in the above "exemplary method" section of the present specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not intended to be limited to the details disclosed herein as such.
The block diagrams of the devices, apparatuses, devices, systems referred to in this application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent to the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. The molecular docking method based on the quantum approximation optimization algorithm is characterized by comprising the following steps of:
obtaining a binding site for a protein receptor and a drug ligand;
calculating a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites;
constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand; the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and
and solving a maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain a butting result of the protein receptor and the drug ligand.
2. The quantum-based approximate optimization algorithm molecular docking method according to claim 1, wherein constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand comprises:
establishing an initial interaction diagram; wherein the number of vertices of the initial interaction graph is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand; and
and determining the connection state of each pair of vertexes in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand so as to obtain the binding interaction diagram.
3. The quantum-based approximate optimization algorithm molecular docking method according to claim 2, wherein the determining the connection state of each pair of vertices in the initial interaction map according to the binding site distance map of the protein receptor and the binding site distance map of the drug ligand to obtain the binding interaction map comprises:
determining the connection state of each pair of vertices in the initial interaction diagram according to the binding site distance diagram of the protein receptor and the binding site distance diagram of the drug ligand; and
and setting corresponding weights according to potential energy of each vertex in the initial interaction diagram to obtain the binding interaction diagram.
4. The quantum-based approximate optimization method according to claim 1, wherein after the maximum weighted graph of the binding interaction graph is solved by the quantum-based approximate optimization algorithm to obtain the result of the docking of the protein receptor and the drug ligand, the quantum-based approximate optimization method further comprises:
and verifying and evaluating the butt joint result.
5. The quantum-based approximately optimized algorithm molecular docking method of claim 1, wherein the obtaining binding sites for protein receptors and drug ligands comprises:
the binding sites for the protein receptor and the drug ligand are selected by a protein ligand complex, or experimental data, or software package.
6. The quantum-based approximately optimized algorithm molecule docking method of claim 1, wherein prior to the obtaining of the binding sites for the protein receptor and the drug ligand, the quantum-based approximately optimized algorithm molecule docking method further comprises:
selecting the drug ligand from drug molecules to be screened; wherein the structure of the drug ligand is obtained by an experimental method.
7. The quantum-based approximately optimized algorithm molecule docking method of claim 1, wherein prior to the obtaining of the binding sites for the protein receptor and the drug ligand, the quantum-based approximately optimized algorithm molecule docking method further comprises:
selecting said protein receptor from a receptor library; wherein the structure of the protein receptor is obtained by experimental methods.
8. Molecular docking device based on quantum approximation optimization algorithm, which is characterized by comprising:
a site acquisition module for acquiring binding sites for a protein receptor and a drug ligand;
a distance calculation module for calculating a binding site distance map of the protein receptor and the drug ligand, respectively, based on the binding sites;
an interactive binding module for constructing a binding interaction map of the protein receptor and the drug ligand based on the binding site distance map of the protein receptor and the binding site distance map of the drug ligand; the number of vertexes of the binding interaction diagram is equal to the product of the number of pharmacophores of the protein receptor and the number of pharmacophores of the drug ligand, and potential energy weight is set at each vertex of the binding interaction diagram; and
and the result solving module is used for solving the maximum weighted graph of the binding interaction graph by adopting a quantum approximation optimization algorithm to obtain the butt joint result of the protein receptor and the drug ligand.
9. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the quantum approximation optimization algorithm based molecular docking method according to any one of the preceding claims 1-7.
10. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to perform the quantum-based approximate optimization algorithm molecular docking method of any of the above claims 1-7.
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