Method and electronic device for molecular docking

Abstract

Embodiments of the present disclosure relate to a method for molecular docking and an electronic device. The method comprises: determining a first binding site on a first molecular surface of a first molecule and a second binding site on a second molecular surface of a second molecule based on a first time-dependent multi-scale feature of the first molecule and a second time-dependent multi-scale feature of the second molecule; obtaining a first chemical feature of the first binding site and a second chemical feature of the second binding site; determining a functional mapping matrix between the first chemical feature and the second chemical feature through functional mapping; determining a correspondence between the first binding site and the second binding site based on the functional mapping matrix; and docking the first molecule and the second molecule through the first binding site and the second binding site based on the correspondence. In this way, the molecular docking scheme does not need to be implemented through a large number of samplings, and therefore the three-dimensional structure generated after docking can be determined more quickly and more efficiently.

Description

Technical Field

[0001] This disclosure generally relates to the fields of computer science and bioinformatics, and more specifically to methods and electronic devices for molecular docking. Background Technology

[0002] Interactions between biomolecules are a crucial foundation for their biological activity. For example, the human body can produce antibody proteins that bind to invading viruses to inhibit disease. In biopharmaceutical research, analyzing known biomolecules that can bind to each other helps to understand the physical and chemical mechanisms of intermolecular interactions, thereby aiding in the design of novel drug molecules that can bind to specific targets (such as in the development of COVID-19 antibodies). Molecular docking is an important research direction in this process.

[0003] One existing approach involves using massive sampling to determine potential binding sites for molecular docking, and then docking the molecules. However, this approach is costly and time-consuming, resulting in poor molecular docking performance. Summary of the Invention

[0004] According to an example embodiment of this disclosure, a method for molecular docking is provided, which determines binding sites based on time-dependent evolution multi-scale features and achieves molecular docking through functional mapping.

[0005] In a first aspect of this disclosure, a method for molecular docking is provided, comprising: determining a first binding site on the surface of a first molecule and a second binding site on the surface of a second molecule based on a first time-dependent evolutionary multiscale feature of a first molecule and a second time-dependent evolutionary multiscale feature of a second molecule; acquiring a first chemical feature of the first binding site and a second chemical feature of the second binding site; determining a functional mapping matrix between the first chemical feature and the second chemical feature through functional mapping; determining a correspondence between the first binding site and the second binding site based on the functional mapping matrix; and docking the first molecule and the second molecule through the first binding site and the second binding site based on the correspondence.

[0006] In a second aspect of the present disclosure, an electronic device is provided, comprising: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method described in the first aspect of the present disclosure when executed by the at least one processing unit.

[0007] In a third aspect of the present disclosure, a computer-readable storage medium is provided having machine-executable instructions stored thereon, which, when executed by a device, cause the device to perform the method described in the first aspect of the present disclosure.

[0008] In a fourth aspect of the present disclosure, a computer program product is provided, including computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method described in the first aspect of the present disclosure.

[0009] In a fifth aspect of this disclosure, an electronic device is provided, including: a processing circuit configured to perform the method described in the first aspect of this disclosure.

[0010] The summary section is provided to introduce a series of concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify key or essential features of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0012] Figure 1 A schematic flowchart illustrating example processes according to some embodiments of the present disclosure is shown;

[0013] Figure 2A A schematic diagram of the electron density field of a benzene molecule according to some embodiments of the present disclosure is shown;

[0014] Figure 2B A schematic diagram of a molecular surface represented by triangulation according to some embodiments of the present disclosure is shown;

[0015] Figure 3A A schematic diagram is shown of nodes that project chemical information of atoms onto the surface of molecules according to some embodiments of the present disclosure;

[0016] Figure 3B A schematic diagram of the electrostatic potential energy function of a molecular surface according to some embodiments of the present disclosure is shown;

[0017] Figure 4 A schematic diagram showing the distribution of the first six eigenfunctions of a molecule on the molecular surface according to some embodiments of the present disclosure is illustrated.

[0018] Figure 5A schematic diagram illustrating the change of thermal distribution on a molecular surface over time according to some embodiments of the present disclosure is shown;

[0019] Figure 6 A schematic diagram illustrating the use of cross-attention networks according to some embodiments of the present disclosure is shown;

[0020] Figure 7 A schematic diagram of molecular docking according to some embodiments of the present disclosure is shown;

[0021] Figure 8 Block diagrams of example apparatuses according to some embodiments of the present disclosure are shown; and

[0022] Figure 9 A block diagram of an example device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] As mentioned above, molecular docking is an important area of ​​research in biomolecules. For example, molecular docking can be achieved through computer modeling to simulate how two molecules interact and bind in real organisms.

[0025] Taking a receptor protein and ligand protein pair as an example, the physicochemical properties and geometric structures of the receptor and ligand proteins can be analyzed, and the ligand protein can be bound to the binding site of the receptor protein. Through docking, the three-dimensional structure of the complex formed by the binding of the receptor and ligand proteins can be predicted. However, current methods cannot efficiently achieve docking between the two molecules.

[0026] To address at least the aforementioned problems and other potential issues, embodiments of this disclosure provide a molecular docking scheme. Specifically, binding sites can be determined based on the time-dependent multi-scale evolution characteristics of the two molecules, allowing molecular docking to be further achieved through functional mapping based on the chemical characteristics of the binding sites. This scheme eliminates the need for extensive sampling, thus enabling faster determination of the resulting three-dimensional structure and greater efficiency.

[0027] Figure 1A schematic flowchart of an example process 100 according to some embodiments of the present disclosure is shown. In block 110, a first binding site on the first molecular surface of the first molecule and a second binding site on the second molecular surface of the second molecule are determined based on a first time-dependent evolutionary multiscale feature of the first molecule and a second time-dependent evolutionary multiscale feature of the second molecule. In block 120, a first chemical feature of the first binding site and a second chemical feature of the second binding site are obtained. In block 130, a functional mapping matrix between the first and second chemical features is determined through functional mapping. In block 140, the correspondence between the first and second binding sites is determined based on the functional mapping matrix. In block 150, based on the correspondence, the first and second molecules are docked through the first and second binding sites.

[0028] Exemplary examples show that the molecules (such as the first molecule and the second molecule) in the embodiments of this disclosure can be biological macromolecules, such as proteins, DNA, etc.; or they can be small molecules, such as small molecules of aspirin. This disclosure is not limited in this respect. For the sake of simplicity, some of the following embodiments are illustrated using proteins as an example.

[0029] In some embodiments, it is understood that, prior to block 110, a first temporal evolutionary multiscale feature of the first molecule and a second temporal evolutionary multiscale feature of the second molecule can be determined respectively. In embodiments of this disclosure, the process of determining the first temporal evolutionary multiscale feature is similar to the process of determining the second temporal evolutionary multiscale feature. The following will combine... Figures 2A to 5 The process of determining the time-dependent evolutionary multiscale features of any molecule is described. It is understood that a similar process can be used to determine the first time-dependent evolutionary multiscale features and the second time-dependent evolutionary multiscale features of a second molecule.

[0030] For example, for any molecule, the molecular surface of the molecule can be determined, wherein the molecular surface is a continuous Riemannian manifold and includes multiple discrete surface nodes; based on the molecular surface, the geometric features of the molecule can be determined; by mapping the atomic information inside the molecule to multiple surface nodes, the surface chemical features of the molecule can be determined; and based on the geometric features and surface chemical features, the time-dependent evolution multiscale features of the molecule can be determined.

[0031] In some exemplary embodiments of this disclosure, the molecular surface of a molecule can be determined based on the isosurface of the electron density field of the molecule.

[0032] Biomolecules are generally measured in units of 10⁻⁶. -10Using the meter (angstrom) as the unit, at this microscopic scale, biomolecules generally follow the physical laws described by quantum mechanics and statistical mechanics, rather than Newtonian mechanics at the macroscopic scale. From the perspective of microscopic electronic structure, molecules consist of positively charged atomic nuclei and negatively charged electron clouds. Intuitively, molecules can be understood as electron density fields. Different biomolecules have different chemical compositions and 3D geometric structures, thus exhibiting different physicochemical properties. For example, specific drug molecules bind to certain protein receptors in the human body to achieve therapeutic effects. That is to say, different molecules have their unique electron density fields, and therefore, different molecules can be represented by describing the shape and chemical properties of this density field. Specifically, the isosurface of the density field can be determined, which is called the molecular surface of the molecule.

[0033] As an example, such as Figure 2A The electron density field 200 of a benzene molecule according to an embodiment of the present disclosure is shown in FIG2, where curve 210 represents an isosurface.

[0034] For example, the analyzed electron density field can be represented as the electron density function of a molecule. Optionally, the electron density function of a molecule can be determined by quantum chemical simulations. Further, the molecular surface can be determined based on the isosurface of the electron density function of the molecule. For example, the electron density function of a molecule may have multiple isosurfaces, and in some embodiments of this disclosure, the molecular surface can be determined by selecting one of these isosurfaces.

[0035] In some exemplary embodiments of this disclosure, the molecular surface can also be determined using other molecular surface calculation methods. For example, the molecular surface can be determined using MSMS calculation software.

[0036] In some exemplary embodiments of this disclosure, the molecular surface of a molecule can also be determined based on sampling of the solvent-accessible or inaccessible surfaces of the molecule.

[0037] It is understood that in other examples, other methods may be used to determine the molecular surface of the molecule in the embodiments of this disclosure, and this disclosure is not limited thereto.

[0038] In some examples, a molecular surface can be represented as a discrete set of nodes and the connections between them. Exemplarily, surface information can be further determined based on the defined molecular surface. For instance, mesh representation methods such as triangulation can be used to store the surface information. Figure 2BA schematic diagram of a molecular surface represented by triangulation is shown. As shown, the surface is shown with triangulation nodes (referred to as "nodes"), and these nodes may be connected. That is, the molecular surface includes multiple surface nodes, such as multiple triangulation nodes.

[0039] Exemplarily, the surface encapsulates molecules and can represent the shape of the molecules. In embodiments of this disclosure, the stored surface information may include: atomic information within the molecule, and the 3D coordinates of each node on the molecular surface and the connections between nodes. For example, the atomic information within the molecule includes the three-dimensional coordinates of atoms and related chemical information such as atom types. It is understood that the molecular surface is a two-dimensional Riemannian manifold, which is itself continuous and smooth. In subsequent processing of embodiments of this disclosure, this continuous and smooth Riemannian manifold can be discretized, for example, by triangulating the nodes.

[0040] In some exemplary embodiments of this disclosure, for each of a plurality of surface nodes, the chemical environment characteristics of the node are obtained by mapping the atomic information of a plurality of atoms associated with the node to the node; based on the chemical environment characteristics of each of the plurality of surface nodes, a fully connected neural network is used to determine the chemical characteristics. Exemplarily, the plurality of atoms associated with the node may include: a plurality of atoms whose distance from the node is less than a distance threshold. Or, exemplarily, the plurality of atoms associated with the node may include: a fixed number of neighboring atoms (e.g., 8 nearest neighbor atoms) closest to the node. For example, the atoms may be sorted according to their distance from the node, and a fixed number (e.g., 8) of the nearest neighbors may be determined from the sorted atoms.

[0041] Specifically, the chemical potential distribution on a molecular surface can be determined based on the surface information of the molecule. Alternatively, the chemical potential distribution can also be referred to as the chemical function distribution, such as the electrostatic potential distribution.

[0042] For example, for any node on an analytical surface, the distances between that node and all atoms within a specific distance range can be determined. For instance, atoms within a distance threshold range can be termed neighboring atoms. Subsequently, the angle between each neighboring atom and the normal to the tangent plane of the surface containing the node, as well as the corresponding atom type, can be determined, serving as an initial representation of the node's chemical environment. For example, the chemical function distribution of a molecular surface can be extracted using a fully connected neural network. That is, a fully connected neural network can learn the representation of the chemical environment surrounding a surface node.

[0043] In this way, by mapping (also known as projecting) the chemical information of the internal atoms onto the nodes on the surface, the chemical information of the entire molecule can be characterized through the nodes on the molecular surface. Figure 3AThis diagram illustrates nodes that project the chemical information of atoms onto the surface of molecules. (Example) Figure 3A As shown, for node 310, atoms within a specific distance range 320 can be identified. The chemical information of the identified atoms can then be projected onto node 310 to determine an initial representation of the chemical environment of node 310, such as the chemical environment characteristics of the node.

[0044] It should be noted that in the embodiments of this disclosure, the chemical information of atoms can be used to update the chemical representation of nodes on the molecular surface. However, the node information does not feed back or change the chemical information of atoms; that is, this projection belongs to a one-way information transmission relationship. This differs from the bidirectional updating of molecular graph neural networks. It is understood that although graph neural networks can achieve long-distance information exchange through graph information transmission, this exchange mechanism is inefficient when the number of nodes is large (e.g., the triangulation representation of a molecule's surface typically has tens of thousands of nodes). Conversely, the embodiments of this disclosure, through the one-way information transmission relationship from atomic information to nodes, can improve the processing efficiency of information exchange.

[0045] For example, a fully connected neural network can be used to determine the chemical characteristics of a molecular surface based on the chemical environment characteristics of each node in a multi-surface network. Alternatively, as an example, the chemical information of atoms can be represented as a multi-dimensional (e.g., 5-dimensional) array, and the surface chemical characteristics can be represented as a multi-dimensional (e.g., 16-dimensional) array.

[0046] Figure 3B A schematic diagram of the electrostatic potential energy function 330 of a molecular surface is shown. For example, this electrostatic potential energy function can be obtained by extraction based on, for example, the first dimension of a chemical feature in a 16-dimensional array. It is understood that, although... Figure 3B The electrostatic potential energy function is used as an example for illustration, but the embodiments disclosed herein are not limited to this. For example, users can customize other chemical information, or learn other chemical representations through neural networks or other means.

[0047] In this way, the distribution of chemical potential on the molecular surface can simultaneously contain both geometric and chemical information. For example, the distribution of chemical potential functions, such as the electrostatic potential function, on the molecular surface belongs to the surface Riemannian manifold space representation of the molecule; that is, chemical information can exist in the surface Riemannian manifold space of the molecule in the form of functions. In other words, in the embodiments of this disclosure, the surface of the molecule is regarded as a continuous and smooth Riemannian manifold space, and chemically related functions are defined in this two-dimensional manifold space.

[0048] In some exemplary embodiments of this disclosure, the geometric features may include one or more of the following: thermal kernel feature function, wave kernel feature function, Gaussian curvature of the molecular surface, or average curvature of the molecular surface.

[0049] For example, the eigenfunctions (or simply Laplace eigenfunctions) and eigenvalues ​​of the Laplace operator on the molecular surface (Riemannian manifold) can be determined, and the thermal core characteristic function and / or wave core characteristic function can be determined based on the eigenfunctions and eigenvalues.

[0050] For example, the eigenfunctions and eigenvalues ​​of the Laplace-Beltramioperator on the Riemannian manifold of each molecular surface can be determined as follows: (1)

[0051] Δφ i =λ i φ i (1)

[0052] In equation (1), Δ represents the Laplace operator, and its meaning is as follows in equation (2):

[0053]

[0054] In equation (1), Let λ represent the i-th eigenfunction. i Let represent the i-th eigenvalue. In equation (2), Let f denote the gradient operator, and let f denote any function distributed on the Riemannian manifold. Eigenfunctions can be determined using known algorithms (e.g., SciPy numerical computation software) or algorithms developed in the future, but this disclosure is not limited in this regard.

[0055] In some examples, the Laplace eigenfunctions and their corresponding eigenvalues ​​of each molecular surface manifold are unique and depend only on the shape of the molecule itself, unaffected by the molecule's position and orientation in three-dimensional space. Therefore, the eigenfunctions of Riemannian manifolds are also referred to as "shape DNA." For each molecule's surface manifold, all its eigenfunctions and eigenvalues ​​can be determined. Exemplarily, the eigenvalues ​​can be further sorted by magnitude, for example, in ascending order, and then the first k eigenvalues ​​(e.g., k = 100 or other values) can be selected, thus reducing computational complexity.

[0056] Understandably, different biomolecules have different shapes, and therefore different surface manifold eigenfunctions. Figure 4 The diagram illustrates the distribution of the first six eigenfunctions of a molecule on its surface according to some embodiments of the present disclosure. Exemplarily, the first six eigenfunctions are distributed on... Figure 4 The middle is shown as In some examples, the eigenfunctions are in Figure 4The region exhibits regional fluctuations, and correspondingly, the eigenfunctions can be understood as Fourier basis functions in two-dimensional manifold space (for example, they can be understood as two-dimensional standing waves), which correspond to sine and cosine functions on a one-dimensional straight line.

[0057] In some exemplary embodiments of this disclosure, geometric features can be represented as geometric characteristic functions. The geometric characteristic functions of a molecular surface can be determined based on the eigenfunctions and eigenvalues ​​of the Laplacian operator on the molecular surface manifold. Optionally, the geometric characteristic functions may include heat kernel signature (HKS) and / or wave kernel signature (WKS).

[0058] For example, it can be based on the aforementioned determined eigenfunctions and eigenvalues ​​λ i The HKS and WKS are constructed as follows:

[0059]

[0060]

[0061] In equations (3) and (4), t and ∈ represent time and energy, respectively, which can be set by the user.

[0062] Optionally, the geometric characteristic functions of the molecular surface may also include the Gaussian curvature and / or mean curvature on the molecular surface (Riemannian manifold). It is understood that the Gaussian curvature and mean curvature can be calculated geometrically, which will not be elaborated upon here.

[0063] In some exemplary embodiments of this disclosure, a unified molecule characteristic can be determined by integrating geometric and chemical characteristics. For example, if the geometric characteristics are represented as geometric characteristic functions and the chemical characteristics are represented as chemical potential distributions, then a unified molecule surface characteristic can be determined based on the chemical potential distribution and the geometric characteristic functions of the molecule surface. This unified characteristic (e.g., represented as a surface characteristic function) represents an integration of chemical and geometric information.

[0064] For example, a fully connected neural network can be used to integrate the chemical and geometric features of each node to obtain a surface feature function for each node. For instance, assuming the chemical features are represented as a 16-dimensional array and the geometric features as a 32-dimensional array, a fully connected neural network can non-linearly transform the chemical and geometric features into a 64-dimensional surface feature function. It is understood that the dimension of the surface feature function is not limited to 64 dimensions; it can be user-defined, such as 128 dimensions or other dimensions, and this disclosure does not limit it.

[0065] For example, a fully connected neural network can be trained on a molecular dataset, specifically, the molecular dataset being related to the application scenario of the embodiments of this disclosure (e.g., a downstream prediction task).

[0066] In some exemplary embodiments of this disclosure, time-dependent evolutionary multi-scale features can be determined based on a unified feature model using a time-dependent evolutionary neural network model. For example, the time-dependent evolutionary multi-scale features represent the multi-scale features of the molecular surface.

[0067] For example, the time-dependent evolutionary neural network model includes an evolutionary operator that is at least based on a Laplace operator and / or on a surface potential term.

[0068] For example, time-dependent evolution operators can be applied to surface feature functions to obtain functions that characterize multi-scale features. For instance, a time-dependent evolution operator can be represented as... or in For Hamiltonian operators, such as Δ represents the Laplace operator, and V represents the surface potential term. For example, the surface potential term V can be a function distribution on a user-defined manifold.

[0069] In some embodiments, when the time-dependent evolution operator is represented as For the initial function u0, the function distribution at time t can be determined by the following equation (5):

[0070]

[0071] To simplify the example, we can assume V = 0, so equation (5) can be simplified to equation (6) as follows:

[0072] u t =e -iΔt u0 (6)

[0073] Equation (6) describes the change of an initial function u0 in the manifold space (i.e., the molecular surface) over time. By controlling different evolution times t, the new function distribution u after evolving over different times can be obtained. t It is understandable that the u obtained from equation (6) t It is a complex number, while the input u0 is a real number. In practice, u can be... t Modulo, thus obtaining the result with u t The corresponding real number.

[0074] Because different molecules have different geometric structures, their Riemannian manifold spaces are also unique, and the evolution of the function u0 on different manifolds is determined by that manifold space. Therefore, the evolved function can serve as a new representation of molecular information, and this representation contains both global and local information about the manifold.

[0075] In other embodiments, when the time-dependent evolution operator is represented as And when V = 0, for the initial function v0, the function distribution at time t can be determined by the following equation (7):

[0076] v t =e -Δt v0 (7)

[0077] Equation (7) can be understood as replacing the imaginary time-dependent evolution operator in equation (6) above with the real time-dependent evolution operator (removing i). It can be understood that equation (6) belongs to the quantum mechanical framework, while equation (7) belongs to the classical mechanical framework. In practical applications, both frameworks can be used to realize the Riemannian manifold representation of molecules.

[0078] In embodiments of this disclosure, the initial function u0 or v0 can be the aforementioned unified feature, i.e., the surface feature function of the molecule. In this way, embodiments of this disclosure can obtain time-dependent evolutionary multi-scale features based on time-dependent evolution operators, i.e., u t or v t .

[0079] For example, the time-dependent evolution operator e in equation (7) -Δt This can be called a heat operator, which describes the distribution of the initial heat distribution v0 in the manifold space after time t. t .

[0080] As an example, Figure 5 A schematic diagram showing the change in heat distribution on the molecular surface over time is shown. It can be understood that this change can be quantitatively described by a time-dependent evolution process as shown in equation (7).

[0081] from Figure 5 As can be seen, the range of heat transfer increases with time t. Therefore, by controlling different evolution times t, multi-scale information transfer can be achieved in the Riemannian manifold space on the molecular surface (short time corresponds to small-scale information transfer, and long time corresponds to large-scale information transfer). Thus, time-dependent evolution-based neural networks can be used to learn the geometric and chemical information of molecules at different scales, thereby improving the ability to represent molecules.

[0082] In embodiments of this disclosure, the eigenfunctions and eigenvalues ​​of the Laplace operator are described above in conjunction with equation (1). Therefore, the time-dependent evolution operator can be based on the eigenfunctions and eigenvalues ​​of the Laplace operator on the Riemannian manifold. Based on this, equation (7) can be further expressed as equation (8) below:

[0083]

[0084] Similarly, equation (6) can be further expressed as equation (9) as follows:

[0085]

[0086] In this way, embodiments of the present disclosure enable time-dependent evolution in the eigenspace by using the eigenfunctions and eigenvalues ​​of the Riemannian manifold and its Laplace operator, which is more efficient than operations in the real space.

[0087] As mentioned above, the uniform features can be represented, for example, as 64-dimensional surface feature functions. That is, each node on the molecular surface can be represented by a 64-dimensional array to represent the uniform features of that node. Then, based on Equation (8) or Equation (9), time-dependent masking can be performed on the 64-dimensional functions respectively. It is understood that each function can have its unique evolution time; for example, t can be used as a parameter of the neural network used for time-dependent evolution or can be set by the user. After time-dependent evolution, multi-scale features on the molecular surface can be obtained, including a series of geometric and chemical features at various scales.

[0088] In the embodiments of this disclosure, a Riemannian manifold-based molecular representation method, distinct from existing molecular representation methods, is provided through time-dependent evolutionary multi-scale features. These time-dependent evolutionary multi-scale features include both geometric and chemical characteristics of the molecule, enhancing the ability to describe molecular features.

[0089] Continue to refer to Figure 1 At box 110, binding sites can be determined based on time-dependent evolutionary multi-scale features. It can be understood that a binding site is a region on a molecule that can bind to another molecule.

[0090] Additionally or optionally, a cross-attention network can be further used. It is understood that a cross-attention network enables information exchange between two molecules. For example, for each node on the first molecule's surface, the attention to each node on the second molecule's surface can be calculated separately. This attention can be the inner product of the features of the two different nodes, reflecting the "correlation" between them. Subsequently, the attention can be normalized, and the features of each node can be updated through cross-attention, for example, using the features of nodes on the first molecule's surface to update the features of nodes on the second molecule's surface, and vice versa. Figure 6 A schematic diagram using a cross-attention network is shown. (See reference...) Figure 6 By combining temporal evolutionary neural networks and cross-attention networks, the temporal evolutionary multi-scale features of two molecules can be obtained, which can then be used for subsequent prediction of binding sites.

[0091] For example, assuming the first molecule is a receptor protein and the second molecule is a ligand protein, at least one node among multiple surface nodes on the molecular surface of the second molecule can be determined based on the second time-dependent evolution multi-scale characteristics of the second molecule. This at least one node indicates a binding site with the first molecule. For instance, a first region (e.g., a partial or complete region of the molecular surface of the second molecule) can be obtained, and for each surface node in this first region, it can be analyzed whether each node can bind to the first molecule, thereby achieving binary prediction.

[0092] In some examples, the first binding site is a subregion (e.g., referred to as the first subregion) of the first molecule's surface, and the second binding site is another subregion (e.g., referred to as the second subregion) of the second molecule's surface. Exemplarily, the first subregion can be represented as a Riemannian manifold. The second subregion is represented as a Riemannian manifold.

[0093] As described above, in the process of describing the time-dependent evolution of molecules across multiple scales, the surface chemical features of the molecules can be obtained. Correspondingly, it can be understood that at box 120, the first chemical feature can be obtained based on the surface nodes included in the first sub-region and the first surface chemical feature of the first molecule. Similarly, the second chemical feature can be obtained based on the surface nodes included in the second sub-region and the second surface chemical feature of the second molecule.

[0094] In the embodiments of this disclosure, the first chemical feature and the second chemical feature have the same properties, such as an electrostatic potential energy function.

[0095] In some examples, chemical features can be represented as linear combinations of their corresponding eigenfunctions. For instance, a first chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemannian manifold of a first subregion, and a second chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemannian manifold of a second subregion. To simplify the description, the first and second chemical features can be represented as follows:

[0096]

[0097] In equation (10), Let the eigenfunctions of the Laplace operator on the Riemannian manifold of the first subregion be denoteed. The sub-expression represents the eigenfunction of the Laplace operator on the Riemannian manifold of the second subregion, while a i and b i These are the coefficients of the linear combination.

[0098] Based on the first and second chemical characteristics, the first coefficient matrix (e.g., denoted as A) and the second coefficient matrix (e.g., denoted as B) can be determined accordingly. Furthermore, the functional mapping matrix can be determined based on the first and second coefficient matrices.

[0099] For example, a functional mapping matrix (such as C) can be represented as

[0100]

[0101] Or CA = B (12)

[0102] For example, the functional mapping matrix can be a kernel function, which can determine the correspondence between the first sub-region and the second sub-region. That is, it can determine which node in the second sub-region corresponds to each node in the first sub-region. For instance, for a given node in the first sub-region, the position of its corresponding node in the second sub-region can be determined.

[0103] In some examples, at box 150, the correspondence can be transformed into corresponding translation and rotation operations using geometric algorithms, thereby achieving docking. For example, docking a ligand protein to a receptor protein. This allows for accurate prediction of the protein complex structure.

[0104] Taking an example where the first molecule is a receptor protein and the second molecule is a ligand protein, Figure 7 A schematic diagram of molecular docking according to an embodiment of the present disclosure is shown.

[0105] It is understood that although the docking embodiments are described in conjunction with the first and second molecules in process 100, the embodiments of this disclosure can be applied to a wider range of molecules. For example, if the first molecule is a virus, proteins with docking potential with the virus can be screened from a large database of known protein structures. Optionally, the binding regions of the selected proteins can be further optimized based on the binding sites to enhance their binding ability to the virus, thereby enabling the faster development of effective antibody drugs.

[0106] It should be understood that in the embodiments of this disclosure, "first," "second," "third," etc., are only used to indicate that multiple objects may be different, but at the same time, it does not exclude that two objects are the same, and should not be interpreted as any limitation on the embodiments of this disclosure.

[0107] It should also be understood that the manner, situation, category, and division of embodiments in the present disclosure are for the convenience of description only and should not constitute a special limitation. Various manners, categories, situations, and features in the embodiments can be combined with each other where logically consistent.

[0108] It should also be understood that the foregoing is merely to help those skilled in the art better understand the embodiments of this disclosure, and is not intended to limit the scope of the embodiments of this disclosure. Those skilled in the art can make various modifications, variations, or combinations based on the foregoing. Such modifications, variations, or combinations are also within the scope of the embodiments of this disclosure.

[0109] It should also be understood that the above description focuses on highlighting the differences between the various embodiments. Similarities or commonalities can be referenced or learned from each other, and for the sake of brevity, they will not be repeated here.

[0110] Figure 8 A schematic block diagram of an example device 800 according to some embodiments of the present disclosure is shown. Device 800 can be implemented by software, hardware, or a combination of both. Figure 8 As shown, the device 800 includes a binding site determination module 810, a chemical feature acquisition module 820, a functional mapping matrix determination module 830, a correspondence determination module 840, and a docking module 850.

[0111] The binding site determination module 810 is configured to determine the first binding site on the first molecule's surface and the second binding site on the second molecule's surface based on the first time-dependent evolutionary multi-scale features of the first molecule and the second time-dependent evolutionary multi-scale features of the second molecule. The chemical feature acquisition module 820 is configured to acquire the first chemical feature of the first binding site and the second chemical feature of the second binding site. The functional mapping matrix determination module 830 is configured to determine the functional mapping matrix between the first and second chemical features through functional mapping. The correspondence determination module 840 is configured to determine the correspondence between the first and second binding sites based on the functional mapping matrix. The docking module 850 is configured to dock the first and second molecules through the first and second binding sites based on the correspondence.

[0112] In some embodiments, the apparatus 800 further includes: a surface node determination module configured to determine a first molecular surface of the first molecule, the first molecular surface being a continuous Riemannian manifold and the first molecular surface including a discrete plurality of surface nodes; a geometric feature determination module configured to determine a first geometric feature of the first molecule based on the first molecular surface; a surface chemical feature determination module configured to determine a first surface chemical feature of the first molecule by mapping atomic information inside the first molecule to the plurality of surface nodes; and a time-dependent evolution multi-scale feature determination module configured to determine a first time-dependent evolution multi-scale feature of the first molecule based on the first geometric feature and the first surface chemical feature.

[0113] For example, the surface node determination module is configured to determine the surface of the first molecule based on the isosurface of the electron density field of the first molecule. Alternatively, the surface node determination module is configured to determine the surface of the first molecule based on sampling of the solvent-accessible or inaccessible surfaces of the first molecule.

[0114] Optionally, the first geometric feature includes at least one of the following: a thermal kernel feature function determined based on the eigenfunctions and eigenvalues ​​of the Laplacian operator of the first molecular surface, a wave kernel feature function determined based on the eigenfunctions and eigenvalues ​​of the Laplacian operator of the first molecular surface, the Gaussian curvature of the first molecular surface, or the average curvature of the first molecular surface.

[0115] For example, the surface chemical feature determination module includes: a chemical environment feature determination submodule, configured to obtain the chemical environment feature of a node for each of a plurality of surface nodes by mapping the atomic information of a plurality of atoms associated with the node to the node; and a surface chemical feature determination submodule, configured to determine a first surface chemical feature based on the chemical environment feature of each of the plurality of surface nodes using a fully connected neural network.

[0116] For example, the time-dependent evolution multi-scale feature determination module is configured to determine a unified feature of a first molecule by integrating a first geometric feature with a first surface chemical feature; and to determine a first time-dependent evolution multi-scale feature based on the unified feature based on a time-dependent evolution neural network model.

[0117] Optionally, the time-dependent evolutionary neural network model includes an evolutionary operator, which is determined based on at least one of the following: the eigenfunctions of the Laplace operator on the Riemann manifold, or the surface potential term, where the surface potential term is a user-defined function distribution on the Riemann manifold.

[0118] In some embodiments, the binding site determination module 810 may be configured to determine a first binding site and a second binding site by using a cross-attention network.

[0119] For example, the first chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemannian manifold of the first binding site, and the second chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemannian manifold of the second binding site.

[0120] In some embodiments, the functional mapping matrix determination module 830 is configured to determine a first coefficient matrix of a first chemical feature; determine a second coefficient matrix of a second chemical feature; and determine a functional mapping matrix based on the first and second coefficient matrices.

[0121] In some embodiments, the apparatus 800 may further include a complex structure determination module configured to determine the structure of the complex after the first molecule and the second molecule are docked based on the docking between the first binding site and the second binding site.

[0122] Figure 8 The device 800 can be used to achieve the above-mentioned combination. Figures 1 to 7 For the sake of brevity, the process described will not be repeated here.

[0123] The division of modules or units in the embodiments of this disclosure is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. Furthermore, the functional units in the disclosed embodiments may be integrated into one unit, exist as separate physical entities, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0124] Figure 9 A block diagram of an example device 900 that can be used to implement embodiments of the present disclosure is shown. It should be understood that... Figure 9The device 900 shown is merely exemplary and should not be construed as limiting the functionality and scope of the implementation described herein. For example, device 900 can be used to perform the functions described above. Figures 1 to 7 The process described herein. For example, device 900 can be implemented as a classical computer and / or a quantum computer.

[0125] like Figure 9 As shown, device 900 is in the form of a general-purpose computing device. Components of computing device 900 may include, but are not limited to, one or more processors or processing units 910, memory 920, storage devices 930, one or more communication units 940, one or more input devices 950, and one or more output devices 960. Processing unit 910 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 920. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of computing device 900.

[0126] Computing device 900 typically includes multiple computer storage media. Such media can be any available media accessible to computing device 900, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 920 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof). Storage device 930 can be removable or non-removable media and may include machine-readable media, such as flash drives, disks, or any other media capable of storing information and / or data (e.g., training data for training) and accessible within computing device 900.

[0127] The computing device 900 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 9 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 920 may include computer program product 925 having one or more program modules configured to perform various methods or actions of various implementations of this disclosure.

[0128] The communication unit 940 enables communication with other computing devices via a communication medium. Additionally, the components of the computing device 900 can function as a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the computing device 900 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0129] Input device 950 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 960 can be one or more output devices, such as a monitor, speaker, printer, etc. Computing device 900 can also communicate with one or more external devices (not shown) via communication unit 940 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with computing device 900, or with any device that enables computing device 900 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication can be performed via an input / output (I / O) interface (not shown).

[0130] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is provided that stores a computer program thereon, which, when executed by a processor, implements the methods described above.

[0131] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0132] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0133] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0135] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for molecular docking, comprising: Based on the first time-dependent evolution multiscale features of the first molecule and the second time-dependent evolution multiscale features of the second molecule, the first binding site on the first molecular surface of the first molecule and the second binding site on the second molecular surface of the second molecule are determined, wherein the first time-dependent evolution multiscale features and the second time-dependent evolution multiscale features each represent the multiscale features of the molecular surface and are determined based on the geometric features and surface chemical features of the molecular surface; Obtain a first chemical feature of the first binding site and a second chemical feature of the second binding site, wherein the first chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the first binding site, and the second chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the second binding site. The functional mapping matrix between the first chemical feature and the second chemical feature is determined by functional mapping. Based on the functional mapping matrix, the correspondence between the first binding site and the second binding site is determined; as well as Based on the aforementioned correspondence, the first molecule and the second molecule are docked through the first binding site and the second binding site.

2. The method according to claim 1, further comprising: The first molecular surface of the first molecule is determined, wherein the first molecular surface is a continuous Riemannian manifold and the first molecular surface includes a plurality of discrete surface nodes; Based on the surface of the first molecule, determine the first geometric features of the first molecule; The first surface chemical characteristics of the first molecule are determined by mapping the atomic information inside the first molecule to the plurality of surface nodes. as well as Based on the first geometric features and the first surface chemical features, the first time-dependent evolution multiscale features of the first molecule are determined.

3. The method of claim 2, wherein determining the first molecular surface comprises: The surface of the first molecule is determined based on the isosurface of the electron density field of the first molecule; or The surface of the first molecule is determined based on sampling of the solvent-accessible or inaccessible surfaces of the first molecule.

4. The method of claim 2, wherein the first geometric feature comprises at least one of the following: The thermonuclear characteristic function is determined based on the eigenfunctions and eigenvalues ​​of the Laplace operator on the surface of the first molecule. The wave kernel characteristic function is determined based on the eigenfunctions and eigenvalues ​​of the Laplace operator on the surface of the first molecule. The Gaussian curvature of the surface of the first molecule, or The average curvature of the surface of the first molecule.

5. The method of claim 2, wherein determining the first surface chemical characteristic comprises: For each of the plurality of surface nodes, the chemical environment characteristics of the node are obtained by mapping the atomic information of the plurality of atoms associated with the node to the node. Based on the chemical environment characteristics of each of the plurality of surface nodes, a fully connected neural network is used to determine the chemical characteristics of the first surface.

6. The method of claim 2, wherein determining the first time-dependent evolutionary multi-scale feature comprises: By integrating the first geometric feature with the first surface chemical feature, the uniform characteristics of the first molecule are determined; as well as Based on the time-dependent evolutionary neural network model, the first time-dependent evolutionary multi-scale feature is determined based on the unified feature.

7. The method of claim 6, wherein the time-dependent evolutionary neural network model comprises an evolutionary operator, the evolutionary operator being determined based on at least one of the following: The eigenfunctions of the Laplace operator on a Riemannian manifold, or The surface potential energy term is a user-defined function distribution on a Riemannian manifold.

8. The method according to claim 1, wherein determining the first binding site and the second binding site comprises: The first binding site and the second binding site are determined by using a cross-attention network.

9. The method of claim 1, wherein determining the functional mapping matrix between the first chemical feature and the second chemical feature comprises: Determine the first coefficient matrix of the first chemical characteristic; Determine the second coefficient matrix of the second chemical characteristic; The functional mapping matrix is ​​determined based on the first coefficient matrix and the second coefficient matrix.

10. The method according to any one of claims 1 to 9, further comprising: Based on the docking between the first binding site and the second binding site, the structure of the complex formed by the docking of the first molecule and the second molecule is determined.

11. An electronic device, comprising: At least one processing unit; At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform an action when executed by the at least one processing unit, the action including: Based on the first time-dependent evolution multiscale features of the first molecule and the second time-dependent evolution multiscale features of the second molecule, the first binding site on the first molecular surface of the first molecule and the second binding site on the second molecular surface of the second molecule are determined, wherein the first time-dependent evolution multiscale features and the second time-dependent evolution multiscale features each represent the multiscale features of the molecular surface and are determined based on the geometric features and surface chemical features of the molecular surface; Obtain a first chemical feature of the first binding site and a second chemical feature of the second binding site, wherein the first chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the first binding site, and the second chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the second binding site. The functional mapping matrix between the first chemical feature and the second chemical feature is determined by functional mapping. Based on the functional mapping matrix, the correspondence between the first binding site and the second binding site is determined; and Based on the aforementioned correspondence, the first molecule and the second molecule are docked through the first binding site and the second binding site.

12. A molecular docking apparatus, comprising: The binding site determination module is configured to determine the first binding site on the first molecular surface of the first molecule and the second binding site on the second molecular surface of the second molecule based on the first time-dependent evolution multi-scale features of the first molecule and the second time-dependent evolution multi-scale features of the second molecule, wherein the first time-dependent evolution multi-scale features and the second time-dependent evolution multi-scale features each represent the multi-scale features of the molecular surface and are determined based on the geometric features and surface chemical features of the molecular surface. The chemical feature acquisition module is configured to acquire a first chemical feature of the first binding site and a second chemical feature of the second binding site, wherein the first chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the first binding site, and the second chemical feature is represented as a linear combination of the eigenfunctions of the Laplace operator on the Riemann manifold of the second binding site. The functional mapping matrix determination module is configured to determine the functional mapping matrix between the first chemical feature and the second chemical feature through functional mapping; The correspondence determination module is configured to determine the correspondence between the first binding site and the second binding site based on the functional mapping matrix; as well as The docking module is configured to dock the first molecule and the second molecule through the first binding site and the second binding site based on the correspondence.

13. A computer-readable storage medium having a computer program stored thereon, the program, when executed by a processor, implementing the method according to any one of claims 1 to 10.

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