Method for determining optimal conformation of molecular docking and related device

Abstract

The embodiment of the application discloses a kind of molecular docking optimal conformation determination method and related device, the method includes the multiple active point position of receptor protein and ligand molecule is combined, obtains multiple complex conformation;According to the non-periodic boundary condition of the complex conformation determines discretization laplace operator;According to the objective function of the complex conformation and the discretization laplace operator determines time evolution operator;The initial quantum state corresponding to the complex conformation is acted on by the time evolution operator, so that the initial quantum state evolves iteratively to target quantum state;According to the target quantum state determines the binding energy of the ligand molecule under multiple complex conformation;The complex conformation of the maximum binding energy is determined as optimal complex conformation.Adopting the embodiment of the application, it is favorable to keep higher calculation precision while, quickly screen out macromolecule and the molecular docking optimal conformation of receptor protein combination.

Description

Technical Field

[0001] This invention relates to the field of quantum simulation technology, and in particular to a method and related apparatus for determining the optimal conformation of molecular docking. Background Technology

[0002] Binding free energy, or molecular binding energy for short, reflects the strength of the interaction between a ligand molecule and a receptor protein. The biological activity of many drug molecules is manifested through their interaction with receptor proteins; therefore, molecular binding energy can be used to characterize the biological activity of ligand molecules. Screening for the optimal molecular docking conformation based on drug molecule activity is crucial for new drug development.

[0003] For smaller receptor proteins or ligand molecules, the number of active sites is relatively small, making it relatively easy to determine their biological activity experimentally. However, for more complex receptor proteins or ligand molecules, the number of active sites is large, making activity experiments very expensive. Traditional simulation methods can balance this cost, but they only maintain good accuracy in smaller systems. As the system grows larger, the computational resources required increase exponentially. Simplifying the computational system can also lead to significant deviations in the calculated binding energy. Summary of the Invention

[0004] This application provides a method and related apparatus for determining the optimal molecular docking conformation, which is beneficial for quickly screening the optimal molecular docking conformation for the binding of macromolecules and receptor proteins while maintaining high computational accuracy.

[0005] The first aspect of this application provides a method for determining the optimal conformation of molecular docking, including:

[0006] Multiple complex conformations are obtained by binding receptor proteins to multiple active sites of ligand molecules.

[0007] The discretized Laplace operator is determined based on the aperiodic boundary conditions of the composite conformation;

[0008] The time evolution operator is determined based on the objective function of the composite conformation and the discretized Laplace operator;

[0009] The time evolution operator is applied to the initial quantum state corresponding to the composite conformation so that the initial quantum state iteratively evolves to the target quantum state;

[0010] The binding energy of the ligand molecules in the multiple composite conformations is determined based on the target quantum state;

[0011] The composite conformation with the highest binding energy is determined as the optimal composite conformation.

[0012] Optionally, determining the discretized Laplace operator based on the aperiodic boundary conditions of the composite conformation includes:

[0013] The number of discrete intervals for each dimension of the solution domain of the composite conformation is determined based on predefined accuracy parameters;

[0014] The discretized Laplace operator is determined based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation.

[0015] Optionally, the discretized Laplacian operator is represented by a matrix, and determining the discretized Laplacian operator based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation includes:

[0016] The solution domain of the composite conformation is extended by adding new elements on both sides of the boundary of the solution domain;

[0017] The initial matrix is ​​determined based on the number of newly added elements and the number of discrete intervals in each dimension of the solution domain;

[0018] The wave function at the newly added element is set to 0, and the initial matrix is ​​adjusted according to the gradient of the boundary element of the solution domain to obtain the discretized Laplace operator. The sign of the gradient of the boundary element is determined according to the non-periodic boundary condition of the composite conformation.

[0019] Optionally, adjusting the initial matrix based on the gradient of the boundary elements of the solution domain includes:

[0020] The gradients of the left boundary elements of the solution domain are determined according to the central difference formula and the second-order difference formula, and the gradients of the right boundary elements of the solution domain are determined according to the second-order backward difference formula.

[0021] The initial matrix is ​​adjusted based on the gradients of the left and right boundary elements of the solution domain.

[0022] Optionally, the aperiodic boundary conditions include a first type of boundary condition and a second type of boundary condition. The first type of boundary condition gives a specific value of the physical quantity on the boundary element, and the second type of boundary condition gives the derivative of the physical quantity on the boundary element. The gradients of the boundary elements of the first type of boundary condition and the second type of boundary condition have opposite signs.

[0023] Optionally, applying the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state, includes:

[0024] Obtain the product of the solution domain dimension of the composite conformation and the predefined precision parameter in qubits;

[0025] Initialize the qubit and prepare the quantum state of the qubit to the initial quantum state;

[0026] The time evolution operator is applied to the initial quantum state to cause the initial quantum state to undergo iterative evolution;

[0027] When the number of iterations equals the number of discrete intervals in each dimension of the solution domain, the measured quantum state is taken as the target quantum state.

[0028] Optionally, determining the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator includes:

[0029] The preset learning rate is used as the time step, and the first damping term corresponding to the discretized Laplacian operator and the second damping term corresponding to the objective function of the composite conformation are determined according to the time step.

[0030] The time evolution operator is determined based on the discretized Laplace operator, the objective function, the first damping term, the second damping term, and the time step.

[0031] A second aspect of this application provides a molecular docking optimal conformation determination apparatus, comprising:

[0032] The complex conformation determination unit is used to bind the receptor protein to multiple active sites of the ligand molecule to obtain multiple complex conformations.

[0033] A Laplacian operator determination unit is used to determine the discretized Laplacian operator based on the aperiodic boundary conditions of the composite conformation.

[0034] A time evolution operator determination unit is used to determine the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator;

[0035] An iterative evolution unit is used to apply the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state;

[0036] A binding energy determination unit is used to determine the binding energy of the ligand molecules in multiple composite conformations based on the target quantum state.

[0037] The optimal composite conformation determination unit is used to determine the composite conformation with the largest binding energy as the optimal composite conformation.

[0038] A third aspect of this application provides an electronic device, including: a processor and a memory;

[0039] The processor is connected to a memory, wherein the memory is used to store computer programs and the processor is used to invoke the computer programs to execute the methods as described in the first aspect of the embodiments of this application.

[0040] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, perform the method as described in the first aspect of this application.

[0041] In the embodiments provided in this application, a discretized Laplace operator is determined based on the non-periodic boundary conditions of the composite conformation obtained by the binding of the receptor protein and the active site of the ligand molecule. Then, a time evolution operator is determined based on the objective function of the composite conformation and the discretized Laplace operator. This allows the time evolution operator to be applied to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state. Furthermore, the binding energy of the ligand molecule under multiple composite conformations is determined based on the target quantum state, and the composite conformation with the largest binding energy is determined as the optimal composite conformation.

[0042] The embodiments provided in this application employ the quantum Hamiltonian descent algorithm, which can be applied to general-purpose quantum computers based on gate circuits and can solve composite conformations with different aperiodic boundary conditions. Therefore, it is advantageous to maintain high computational accuracy while utilizing the superposition and parallel characteristics of quantum computing to quickly screen the optimal molecular docking conformation for the binding of macromolecules and receptor proteins. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 An example system block diagram of a molecular docking optimal conformation determination method provided in one embodiment of this application is shown;

[0045] Figure 2 A flowchart illustrating a method for determining the optimal conformation of molecular docking according to an embodiment of this application is shown.

[0046] Figure 3 A schematic diagram of the structure of a molecular docking optimal conformation determination device provided in one embodiment of this application is shown;

[0047] Figure 4A schematic diagram of the structure of a computer device provided in one embodiment of this application is shown. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] Classical computers use transistors to encode information in binary data, such as bits, where each bit can represent a value of 1 or 0. These 1s and 0s act as switches to drive the functions of a classical computer. If there are n bits of data, there are 2^n possible classical states, and one state is represented at a time.

[0050] Quantum computers use quantum processors that operate on data represented by qubits, also known as quantum bits. A single qubit can represent the classical binary states "0" or "1", or a superposition of "0" and "1". Because it can represent a superposition of "0" and "1", a qubit can represent both "0" and "1" states simultaneously. For example, if there are n bits of data, then 2^n qubits can represent n bits of data. n A quantum state can be represented simultaneously. Furthermore, qubits in a superposition can be correlated with each other, a phenomenon known as entanglement, where the state of one qubit (whether 1, 0, or both) depends on the state of another qubit, and more information can be encoded within two entangled qubits. Based on the principles of superposition and entanglement, qubits enable quantum computers to perform functions that might be relatively complex and time-consuming for classical computers.

[0051] Please refer to Figure 1 This illustrates an example system block diagram of a method for determining the optimal conformation of molecular docking according to an embodiment of this application. System 100 may be a hybrid computing system comprising a combination of one or more quantum computers, quantum systems, and / or classical computers. Figure 1In the example shown, system 100 may include a quantum system 110 and a classical computer 120. In one implementation, the quantum system 110 and the classical computer 120 may be configured to communicate via one or more wired and / or wireless connections (e.g., wireless networks). The quantum system 110 may include a quantum chipset consisting of one or more quantum chips, comprising various hardware components for processing data encoded in qubits. The quantum chipset may be a quantum computing core surrounded by infrastructure to protect the quantum chips from electromagnetic noise sources, mechanical vibration sources, heat sources, and other noise sources that can degrade the performance of the quantum chips. The classical computer 120 may be electronically integrated with the quantum system 110 via any suitable wired and / or wireless electronic connection.

[0052] exist Figure 1 In the example shown, quantum system 110 can be any suitable set of components capable of performing quantum operations on a physical system. Quantum operations, such as quantum gate operations, manipulate the quantum states of qubits to evolve and / or become entangled. Figure 1 In the illustrated example embodiment, the quantum system 110 may include a measurement and control unit 111, an interface 112, and a quantum chip 113. In some embodiments, all or part of each of the measurement and control unit 111, interface 112, and quantum chip 113 may be located in a cryogenic environment to facilitate the performance of quantum operations. The quantum chip 113 may be any hardware capable of processing information using quantum states. This hardware may include multiple qubits and means for coupling or entanglement of the qubits to process information using quantum states. Qubits may include, but are not limited to, charge qubits, flux qubits, phase qubits, spin qubits, and ion qubits. The quantum chip may include a set of quantum logic gates configured to perform quantum logic operations on the qubits stored in a quantum register. The quantum gates may include one or more single-qubit gates, two-qubit gates, and / or other multi-qubit gates.

[0053] The measurement and control unit 111 can be any combination of digital computing devices capable of performing quantum computing (e.g., executing quantum circuits) in conjunction with interface 112. This digital computing device may include a digital processor and memory for storing and executing quantum instructions using interface 112. The digital computing device may also include a communication protocol device for receiving instructions and sending the results of the performed quantum computing to a classical computer. Additionally, the digital computing device may include a communication interface having interface 112. In one embodiment, the measurement and control unit 111 may be configured to receive classical instructions (e.g., from classical computer 120) and convert these classical instructions into measurement and control instructions for interface 112. The measurement and control instructions provided by the measurement and control unit 111 to interface 112 may be, for example, digital signals indicating which quantum gates in a quantum gate array need to be applied to the qubits to perform a specific function. Interface 112 may be configured to convert these digital signals into analog signals (e.g., analog pulses of microwave pulses), which can be used to apply quantum gates to the qubits to manipulate the interactions between the qubits.

[0054] Interface 112 may be a classical-quantum interface, comprising a combination of devices capable of receiving instructions from the integrated measurement and control unit 111 and converting those instructions into a means for implementing quantum operations. In one embodiment, interface 112 may convert instructions from the integrated measurement and control unit 111 into drive signals capable of driving or manipulating qubits, and / or applying quantum gates to qubits. Additionally, interface 112 may be configured to convert signals received from the quantum chip 113 into digital signals capable of being processed and transmitted by the integrated measurement and control unit 111. Devices included in interface 112 may include, but are not limited to, digital-to-analog converters, analog-to-digital converters, waveform generators, attenuators, amplifiers, optical fibers, lasers, and filters. Interface 112 may further include circuitry configured to measure multiple qubits after the application of quantum gates, wherein the measurements may produce results represented in classical bits. Each measurement performed by interface 112 may be read out to a device connected to the quantum system 110, such as a classical computer 120. The multiple measurement results provided by interface 112 may represent probabilistic results.

[0055] The classical computer 120 can include hardware components such as a processor and storage devices (e.g., including memory devices and classical registers) for processing data encoded in classical bits. In one embodiment, the classical computer 120 can be configured to provide the quantum system 110 with various control signals, instructions, and data encoded in classical bits. Further, quantum states measured by the quantum system 110 can be read out by the classical computer 120, and the classical computer 120 can store the measured quantum states as classical bits in classical registers. In one embodiment, the classical computer 120 can be any suitable combination of computer-executable hardware and / or computer-executable software capable of executing the preparation module 121 to perform quantum computation using data stored in the data storage module 122 as part of the construction and computation. The data storage module 122 can be a repository for data to be analyzed using quantum computing algorithms and the results of that analysis. The preparation module 121 can be a program or module capable of preparing classical data from the data storage module 122 as part of a quantum circuit implementation. Preparation module 121 can be instantiated as part of a larger algorithm, such as an application programming interface (API) function call, or by resolving hybrid classical-quantum computing into aspects of quantum and classical computing. For example, preparation module 121 can generate instructions for creating quantum circuits using quantum gates. In an embodiment, such instructions can be stored by the measurement and control unit 111 and can be instantiated by components of interface 112 to execute, enabling quantum operations of quantum gates to be performed on quantum chip 113.

[0056] The classic computer 120 may be a laptop computer, desktop computer, vehicle-integrated computer, smart mobile device, tablet device, and / or any other suitable classic computing device. Additionally or alternatively, the classic computer 120 may also operate as part of a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The classic computer 120 may also reside in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

[0057] Please refer to Figure 2 This illustration shows a flowchart of a method for determining the optimal conformation of molecular docking according to an embodiment of this application. This method can be applied to computer devices, which refer to electronic devices with data computing and processing capabilities. The method may include the following steps:

[0058] Step 201: The receptor protein is bound to multiple active sites of the ligand molecule to obtain multiple complex conformations.

[0059] Receptor proteins are a class of proteins that can recognize and bind to specific signaling molecules (ligands). They play a crucial role in cell signal transduction, transmitting external signals into the cell through ligand binding and triggering a series of biological responses. Receptor proteins exhibit high specificity and affinity, enabling them to accurately recognize and bind to their corresponding ligands.

[0060] Ligand molecules are molecules that can bind to receptors. In biology and chemistry, ligand molecules can specifically bind to receptor proteins, thereby triggering a series of biochemical reactions or altering the chemical properties of substances.

[0061] The active site can be a biologically active group or substance, or a specific atom, ion, or molecule in a compound that can undergo a chemical reaction. Active sites can be located on receptor proteins, ligand molecules, or both.

[0062] Among them, the complex conformation is obtained by binding multiple active sites of the receptor protein and the ligand molecule, which can be obtained through molecular dynamics simulation or molecular docking technology.

[0063] Step 202: Determine the discretized Laplace operator based on the aperiodic boundary conditions of the composite conformation.

[0064] Step 203: Determine the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator.

[0065] In the case of high dimensions, quantum Hamiltonian descent (QHD) is defined as follows:

[0066]

[0067] In the above equation, we assume the reduced Planck constant. φ t and χ t The time-dependent damping term is used to control the overall direction of the simulation. It is typically set in the experiment... This causes the kinetic energy term to gradually decrease, and the system gradually approaches the objective function f(x).

[0068] In a general-purpose quantum computer architecture based on gate circuits, processing continuous problems first requires discretizing time. Consider the simplest first-order Trotter-Suzuki decomposition method:

[0069] e -it(A+B) =e -itA e -itB ,

[0070] Using U(0,T) to represent the time evolution operator, and combining it with the Trotter-Suzuki decomposition above, we have:

[0071]

[0072] in Represents the time-ordering operator, a j and b j Corresponding to QHD and T represents the longest evolution period, Δt represents the smallest time evolution unit after discretization, and j represents the discretization interval index. In application, f is the objective function to be solved (corresponding to the potential energy term in Hamiltonian). To perform time evolution simulation, the Laplace operator needs to be solved, i.e. The Laplace operator is determined based on the aperiodic boundary conditions of the composite conformation. Once the Laplace operator is determined, the time evolution operator can be determined based on the objective function and the discretized Laplace operator.

[0073] Step 204: Apply the time evolution operator to the initial quantum state corresponding to the composite conformation so that the initial quantum state iteratively evolves to the target quantum state.

[0074] The initial quantum state can be a uniform superposition state or a problem-specific starting state. For example, in the embodiments of this application, if there is data on the possible initial positions of the receptor protein or ligand molecule, a specific initial quantum state can be constructed accordingly; if not, a uniform superposition state can be used as the initial quantum state.

[0075] Step 205: Determine the binding energy of the ligand molecules in the multiple composite conformations based on the target quantum state.

[0076] Step 206: Determine the composite conformation with the largest binding energy as the optimal composite conformation.

[0077] For example, a qubit is measured, and the target quantum state is determined based on the measurement result. This target quantum state is an eigenstate of the Hamiltonian. Based on this eigenstate, its corresponding eigenvalue can be determined, which is the binding energy of the ligand molecule in the composite conformation. The greater the binding energy, the greater the biological activity of the ligand molecule and the stronger the interaction between the ligand molecule and the receptor protein. Therefore, the composite conformation with the largest binding energy can be determined as the optimal composite conformation.

[0078] In the embodiments provided in this application, a discretized Laplace operator is determined based on the non-periodic boundary conditions of the composite conformation obtained by the binding of the receptor protein and the active site of the ligand molecule. Then, a time evolution operator is determined based on the objective function of the composite conformation and the discretized Laplace operator. This allows the time evolution operator to be applied to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state. Furthermore, the binding energy of the ligand molecule under multiple composite conformations is determined based on the target quantum state, and the composite conformation with the largest binding energy is determined as the optimal composite conformation.

[0079] The embodiments provided in this application employ the quantum Hamiltonian descent algorithm, which can be applied to general-purpose quantum computers based on gate circuits and can solve composite conformations with different aperiodic boundary conditions. Therefore, it is advantageous to maintain high computational accuracy while utilizing the superposition and parallel characteristics of quantum computing to quickly screen the optimal molecular docking conformation for the binding of macromolecules and receptor proteins.

[0080] In one embodiment provided in this application, determining the discretized Laplace operator based on the aperiodic boundary conditions of the composite conformation includes:

[0081] The number of discrete intervals for each dimension of the solution domain of the composite conformation is determined based on predefined accuracy parameters;

[0082] The discretized Laplace operator is determined based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation.

[0083] For example, if the predefined precision parameter is q, then the number of discrete intervals for each dimension is N = 2. q .

[0084] Optionally, the discretized Laplacian operator is represented by a matrix, and determining the discretized Laplacian operator based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation includes:

[0085] The solution domain of the composite conformation is extended by adding new elements on both sides of the boundary of the solution domain;

[0086] The initial matrix is ​​determined based on the number of newly added elements and the number of discrete intervals in each dimension of the solution domain;

[0087] The wave function at the newly added element is set to 0, and the initial matrix is ​​adjusted according to the gradient of the boundary element of the solution domain to obtain the discretized Laplace operator. The sign of the gradient of the boundary element is determined according to the non-periodic boundary condition of the composite conformation.

[0088] For example, given the variables, the solution domain Ω = [a, b] is calculated. d Here, d represents the dimension of the variable, typically 3 in molecular docking, i.e., three-dimensional space. New elements are added to both sides, i.e., to the left of 'a' and to the right of 'b'. The number of new elements can be pre-defined, and the interval Δx between elements can be determined based on the number N of discrete intervals in each dimension of the solution domain. Alternatively, it can be determined based on a predefined precision parameter q.

[0089] After determining the number of new elements M and the number of discrete intervals N for each dimension of the solution domain, the dimension of the initial matrix can be determined. The dimension of this initial matrix is ​​N + M + 1. For example, with a domain Ω = [0, 2], N = 2, and M = 2, the dimension of the initial matrix is ​​5. Assuming the wavefunction to be evolved is φ, considering a single variable x, and the discretized Laplace operator... It can be represented as:

[0090]

[0091]

[0092] Assuming that after discretization, the domain Ω = [0, 1, 2] is defined, and two new elements -1 and 3 are added, i.e., φ is introduced. -1 And φ3, the above Laplace operator can be expressed as:

[0093]

[0094] The matrix described above is the initial matrix. Observation reveals the following characteristics of the row elements corresponding to the two newly added elements in the initial matrix: the elements in the first row, first column, and the last row, last column are 2; the elements in the first row, second column, and the last row, second to last column are -2. The row elements corresponding to the elements already present in the initial matrix are -1, 2, -1; and the remaining elements are 0. Based on these characteristics, the values ​​of all elements can be determined. Therefore, once the number of new elements M and the number of discrete intervals N in each dimension of the solution domain are determined, the initial matrix can be determined.

[0095] Specifically, adjusting the initial matrix based on the gradients of the boundary elements of the solution domain includes:

[0096] The gradients of the left boundary elements of the solution domain are determined according to the central difference formula and the second-order difference formula, and the gradients of the right boundary elements of the solution domain are determined according to the second-order backward difference formula.

[0097] The initial matrix is ​​adjusted based on the gradients of the left and right boundary elements of the solution domain.

[0098] Considering the first type of boundary condition (Dirichlet boundary condition), and the wavefunction confined within a specified range, assume that the wavefunction is 0 outside the boundary of the simulation space, and remains unchanged at the boundary, i.e., φ. -1 With φ3 equal to 0, the corresponding Laplace matrix becomes:

[0099]

[0100]

[0101] Since the wavefunction outside the boundary is 0, the accuracy at the boundary cannot be guaranteed to be O(a). 2 (Accuracy, considering the central difference and second-order central difference formulas:)

[0102] φ1-φ-1=2aφ′0+O(a)

[0103] φ1-2φ0+φ-1=a 2 φ″0+O(a 2 ),

[0104] Ignoring the error term, adding the two equations above, we have 2φ1 - 2φ0 = 2aφ′0 + a 2 φ″0, therefore the Laplace operator (denoted as L) is adjusted. D )for:

[0105]

[0106] Next, multiply by the wave function:

[0107]

[0108] We can obtain the operator at φ0 as 2φ0 - 2φ1, and from the previous derivation, we can obtain the wave function gradient at the boundary φ0 as... Therefore, an additional first-order differential gradient needs to be added at the boundary. This ensures the accuracy of the result at φ0. At the φ2 boundary, due to the lack of information about φ3, the gradient information can be directly obtained using the second-order backward difference formula and multiplied by...

[0109] φ2-2φ1+φ0=2a 2 φ″2+O(a 2 ),

[0110] Therefore, the Laplace operator is further modified as follows:

[0111]

[0112] The aperiodic boundary conditions include a first type of boundary condition and a second type of boundary condition. The first type of boundary condition gives a specific value of the physical quantity on the boundary element, and the second type of boundary condition gives the derivative of the physical quantity on the boundary element. The gradients of the boundary elements of the first type of boundary condition and the second type of boundary condition have opposite signs.

[0113] For the second type of boundary condition (Neumann boundary condition), the gradient information at the boundary is constrained, while the wavefunction outside the boundary is 0. Here, it is assumed that the gradient at the boundary is in the opposite direction of the original gradient. Referring to the derivation method of the Dirichlet boundary condition, the wavefunction at the boundary is first defined, and then the Laplacian operator is adjusted based on the result.

[0114]

[0115] Since the gradient directions are opposite, we can directly add signs to the corresponding positions of the original operator to obtain the Laplace operator for the Neumann boundary conditions (denoted as L). N )for:

[0116]

[0117] Here, we also need to compensate for the first-order differential gradient at the boundary. and multiplied by

[0118] In one embodiment provided in this application, applying the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state, includes:

[0119] Obtain the product of the solution domain dimension of the composite conformation and the predefined precision parameter in qubits;

[0120] Initialize the qubit and prepare the quantum state of the qubit to the initial quantum state;

[0121] The time evolution operator is applied to the initial quantum state to cause the initial quantum state to undergo iterative evolution;

[0122] When the number of iterations equals the number of discrete intervals in each dimension of the solution domain, the measured quantum state is taken as the target quantum state.

[0123] Specifically, determining the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator includes:

[0124] The preset learning rate is used as the time step, and the first damping term corresponding to the discretized Laplacian operator and the second damping term corresponding to the objective function of the composite conformation are determined according to the time step.

[0125] The time evolution operator is determined based on the discretized Laplace operator, the objective function, the first damping term, the second damping term, and the time step.

[0126] For example, if the solution domain dimension is d and the predefined precision parameter is q, then the number of qubits obtained is dq. These dq qubits are initialized as follows: The quantum state of the qubit is prepared to an initial quantum state |ψ0>, which can be a uniform superposition state or a problem-specific starting state. For example, in the embodiments of this application, if there is data on the possible initial positions of the receptor protein or ligand molecule, a specific initial quantum state can be constructed accordingly; otherwise, a uniform superposition state can be used as the initial quantum state. The iterative formula for the quantum state is as follows:

[0127]

[0128] in For the first damping term, Let V be the second damping term, V|x>=f(x)|x>, L is the Laplace operator for the aperiodic boundary conditions derived earlier, and s is the learning rate. j =ns, n = 0, 1, 2...N, and the total effective simulation time is T = Ns. In molecular docking, f(x) represents the interaction potential energy function between the molecule and the acceptor, which is related to variables such as the molecule's position and orientation x.

[0129] Figure 3 A schematic diagram of a molecular docking optimal conformation determination device according to an embodiment of this application is shown. The device includes:

[0130] The complex conformation determination unit 301 is used to bind the receptor protein to multiple active sites of the ligand molecule to obtain multiple complex conformations.

[0131] The Laplacian operator determination unit 302 is used to determine the discretized Laplacian operator based on the aperiodic boundary conditions of the composite conformation;

[0132] The time evolution operator determination unit 303 is used to determine the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator;

[0133] The iterative evolution unit 304 is used to apply the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state;

[0134] Binding energy determination unit 305 is used to determine the binding energy of the ligand molecules in the plurality of the composite conformations based on the target quantum state;

[0135] The optimal composite conformation determination unit 306 is used to determine the composite conformation with the largest binding energy as the optimal composite conformation.

[0136] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of this application is shown, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the function of the computer system of the molecular docking optimal conformation determination method in any of the above embodiments.

[0137] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, causes the computer to perform the functions of the computer system of the molecular docking optimal conformation determination method in any of the above embodiments.

[0138] This application also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the functions of the computer system in any of the above embodiments for determining the optimal conformation of molecular docking.

[0139] It is understood that the specific examples in this application are only intended to help those skilled in the art better understand the implementation methods of this application, and are not intended to limit the scope of the invention.

[0140] It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not limit the implementation process of the embodiments of this application in any way.

[0141] It is understood that the various implementation methods described in this application can be implemented individually or in combination, and the implementation methods in this application are not limited in this respect.

[0142] Unless otherwise stated, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0143] It is understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0144] It is understood that the memory in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Specifically, non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0149] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0150] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] The above are merely specific embodiments of this application, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for determining the optimal conformation of molecular docking, characterized in that, include: Multiple complex conformations are obtained by binding receptor proteins to multiple active sites of ligand molecules. The discretized Laplace operator is determined based on the aperiodic boundary conditions of the composite conformation; The time evolution operator is determined based on the objective function of the composite conformation and the discretized Laplace operator; The time evolution operator is applied to the initial quantum state corresponding to the composite conformation so that the initial quantum state iteratively evolves to the target quantum state; The binding energy of the ligand molecules in the multiple composite conformations is determined based on the target quantum state; The composite conformation with the highest binding energy is determined as the optimal composite conformation.

2. The method according to claim 1, characterized in that, The step of determining the discretized Laplace operator based on the aperiodic boundary conditions of the composite conformation includes: The number of discrete intervals for each dimension of the solution domain of the composite conformation is determined based on predefined accuracy parameters; The discretized Laplace operator is determined based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation.

3. The method according to claim 2, characterized in that, The discretized Laplacian operator is represented by a matrix. Determining the discretized Laplacian operator based on the number of discrete intervals in each dimension of the solution domain and the aperiodic boundary conditions of the composite conformation includes: The solution domain of the composite conformation is extended by adding new elements on both sides of the boundary of the solution domain; The initial matrix is ​​determined based on the number of newly added elements and the number of discrete intervals in each dimension of the solution domain; The wave function at the newly added element is set to 0, and the initial matrix is ​​adjusted according to the gradient of the boundary element of the solution domain to obtain the discretized Laplace operator. The sign of the gradient of the boundary element is determined according to the non-periodic boundary condition of the composite conformation.

4. The method according to claim 3, characterized in that, The step of adjusting the initial matrix based on the gradient of the boundary elements of the solution domain includes: The gradients of the left boundary elements of the solution domain are determined according to the central difference formula and the second-order difference formula, and the gradients of the right boundary elements of the solution domain are determined according to the second-order backward difference formula. The initial matrix is ​​adjusted based on the gradients of the left and right boundary elements of the solution domain.

5. The method according to claim 3, characterized in that, The aperiodic boundary conditions include a first type of boundary condition and a second type of boundary condition. The first type of boundary condition gives a specific value of the physical quantity on the boundary element, and the second type of boundary condition gives the derivative of the physical quantity on the boundary element. The gradients of the boundary elements of the first type of boundary condition and the second type of boundary condition have opposite signs.

6. The method according to claim 2, characterized in that, The step of applying the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state, includes: Obtain the product of the solution domain dimension of the composite conformation and the predefined precision parameter in qubits; Initialize the qubit and prepare the quantum state of the qubit to the initial quantum state; The time evolution operator is applied to the initial quantum state to cause the initial quantum state to undergo iterative evolution; When the number of iterations equals the number of discrete intervals in each dimension of the solution domain, the measured quantum state is taken as the target quantum state.

7. The method according to any one of claims 1-6, characterized in that, The step of determining the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator includes: The preset learning rate is used as the time step, and the first damping term corresponding to the discretized Laplacian operator and the second damping term corresponding to the objective function of the composite conformation are determined according to the time step. The time evolution operator is determined based on the discretized Laplace operator, the objective function, the first damping term, the second damping term, and the time step.

8. A device for determining the optimal conformation of molecular docking, characterized in that, include: The complex conformation determination unit is used to bind the receptor protein to multiple active sites of the ligand molecule to obtain multiple complex conformations. A Laplacian operator determination unit is used to determine the discretized Laplacian operator based on the aperiodic boundary conditions of the composite conformation. A time evolution operator determination unit is used to determine the time evolution operator based on the objective function of the composite conformation and the discretized Laplace operator; An iterative evolution unit is used to apply the time evolution operator to the initial quantum state corresponding to the composite conformation, so that the initial quantum state iteratively evolves to the target quantum state; A binding energy determination unit is used to determine the binding energy of the ligand molecules in multiple composite conformations based on the target quantum state. The optimal composite conformation determination unit is used to determine the composite conformation with the largest binding energy as the optimal composite conformation.

9. An electronic device, characterized in that, include: Processor and memory; The processor is connected to a memory, wherein the memory is used to store a computer program, and the processor is used to invoke the computer program to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, perform the method as described in any one of claims 1-7.

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