Molecular force field modeling method and device, electronic equipment and storage medium

Abstract

The invention provides a molecular force field modeling method and device, electronic equipment and a storage medium. The method comprises the following steps: establishing a fingerprint spectrum of each atom in a target molecule according to structural feature information and energy feature information of the target molecule; clustering the fingerprint spectrum of each atom in the target molecule to obtain a plurality of atom categories; modeling different types of atom clusters to obtain a potential energy equation between the atom clusters; and establishing a molecular force field according to potential energy equations of different types of atom clusters. The fingerprint spectrum of each atom in the target molecule is clustered, and the clustering process is effectively used for replacing the condition that the atom types are classified by human experience; a molecular network probability graph model is established through a Bayesian method, data mining is carried out, and multi-body energy is simulated, so that the dependence of a molecular force field on fitting data is reduced, and the accuracy of the simulated molecular force field is improved.

Description

Technical Field

[0001] The present application relates to the technical field of structural biology and biomolecular modeling, and in particular, to a molecular force field modeling method, device, electronic device and storage medium. Background Art

[0002] At present, the types of atoms in the molecular force field are mostly defined empirically. In the specific practice, it is found that the classification of atomic types based on chemists' knowledge of chemical molecular structures and the mathematical description of the energy of the interaction between atoms are not objective and accurate enough, because there are limitations at the current cognitive level. The existence of cognitive barriers makes it impossible to objectively classify atoms based on the electron density distribution characteristics in different molecular structures with existing knowledge and human intuition, and to establish mathematical models to accurately calculate the energy of complex target molecular systems under the computing conditions supported by the computing power of existing computers. Based on the existing computing power, most related models use classical Newtonian mechanics formulas to perform molecular energy calculations and describe molecular motions accordingly. This calculation method has cumulative errors in the calculation process, so the description of atomic-level objects between macroscopic and microscopic scales is not accurate enough, that is, the accuracy of the simulated molecular force field is not enough. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a molecular force field modeling method, device, electronic device and storage medium, which are used to improve the problem of insufficient accuracy of the simulated molecular force field.

[0004] The embodiment of the present application provides a molecular force field modeling method, including: establishing a fingerprint map of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule; clustering the fingerprint map of each atom in the target molecule to obtain multiple atomic categories; for each atomic category in the multiple atomic categories, modeling atomic clusters of different categories to obtain potential energy equations between atomic clusters; establishing a molecular force field according to the potential energy equations of atomic clusters of different categories. In the implementation process of the above scheme, by establishing a fingerprint map of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule, and clustering the fingerprint map of each atom in the target molecule, the clustering process is effectively used to replace the situation of classifying atomic types based on human experience, thereby improving the accuracy of the simulated molecular force field.

[0005] Optionally, in an embodiment of the present application, a fingerprint of each atom in the target molecule is established based on the structural characteristic information and energy characteristic information of the target molecule, including: obtaining the total charge distribution of all atoms in the target molecule; projecting the total charge distribution of all atoms in the target molecule onto the Fibonacci sphere lattice constructed for each atom to simulate the charge density distribution on the surface of each atom; and performing dimensionality reduction sorting on the charge density distribution on the surface of each atom of the target molecule to obtain a fingerprint of each atom in the target molecule. In the implementation process of the above scheme, the fingerprint of each atom in the target molecule is obtained by projecting the total charge distribution of all atoms in the target molecule onto the Fibonacci sphere lattice constructed for each atom, and performing dimensionality reduction sorting on the charge density distribution on the surface of each atom of the target molecule. Since there is no need to consider the global configuration of the molecule, the speed of processing the molecular structure and properties is improved.

[0006] Optionally, in an embodiment of the present application, obtaining the total charge distribution of all atoms in the target molecule includes: extracting structural characteristic information of the target molecule and energy characteristic information of the target molecule from a standard complex molecule file; and determining the total charge distribution of each atom in the target molecule based on the structural characteristic information and the energy characteristic information. In the implementation process of the above scheme, by extracting information from the file, the structural characteristic information and energy characteristic information of the target molecule can be obtained more effectively, avoiding the time and resource consumption of conducting experiments or computational simulations, and improving the efficiency and accuracy of determining the total charge distribution.

[0007] Optionally, in an embodiment of the present application, the fingerprint spectrum of each atom in the target molecule is clustered, including: clustering the fingerprint spectrum of each atom in the target molecule according to the charge density distribution of the atom, evaluating the clustering results using a clustering evaluation method, and finally establishing a classification standard.

[0008] Optionally, in an embodiment of the present application, the molecular force field includes: a potential energy equation between atomic clusters; modeling different types of atomic clusters, including: modeling atomic clusters to obtain the Boltzmann probability distribution between atoms in the atomic clusters; determining the potential energy equation between atomic clusters based on the Boltzmann probability distribution between atoms in the atomic clusters. In the implementation of the above scheme, the Boltzmann probability distribution between atoms in the atomic clusters can more accurately describe the interactions within the molecule, thereby effectively improving the accuracy of the potential energy equation.

[0009] Optionally, in an embodiment of the present application, an atomic cluster includes: an atomic pair consisting of two atoms, or an atomic group consisting of three atoms; modeling of different categories of atomic clusters includes: dividing all atoms in the target molecule into core region atoms and background region atoms; removing the influence of the background region atoms on the probability density distribution of the core region atoms by an iterative method, and obtaining a potential energy equation between atomic groups, or a potential energy equation between atomic pairs. In the implementation process of the above scheme, since the iterative method can gradually reduce the influence of the background region atoms on the core region atoms, a more accurate probability density distribution is obtained, thereby effectively improving the accuracy of the potential energy equation.

[0010] Optionally, in an embodiment of the present application, a molecular force field is established according to the potential energy equations of different categories of atomic clusters, including: taking atoms in target molecules as nodes, taking potential energy equations between atomic clusters as edges between nodes, modeling different categories of atomic clusters, and obtaining a molecular force field. In the implementation process of the above scheme, by modeling different categories of atomic clusters, it is possible to more accurately describe the interactions in molecules, thereby improving the modeling precision and accuracy of the molecular force field.

[0011] Optionally, in an embodiment of the present application, the target molecules include: biological molecules and drug-like molecules.

[0012] The embodiment of the present application also provides a molecular force field simulation device, including: a fingerprint map establishment module, which is used to establish a fingerprint map of each atom in the target molecule according to the structural feature information and energy feature information of the target molecule; an atomic category acquisition module, which is used to cluster the fingerprint map of each atom in the target molecule to obtain multiple atomic categories; a potential energy equation acquisition module, which is used to model different categories of atomic clusters for each atomic category in the multiple atomic categories to obtain the potential energy equations between the atomic clusters; and a molecular force field establishment module, which is used to establish a molecular force field according to the potential energy equations of different categories of atomic clusters.

[0013] Optionally, in an embodiment of the present application, a fingerprint map establishment module includes: a charge distribution acquisition submodule, used to obtain the total charge distribution of all atoms in the target molecule; a charge density simulation submodule, used to project the total charge distribution of all atoms in the target molecule onto a Fibonacci sphere lattice constructed for each atom to simulate the charge density distribution on the surface of each atom; a fingerprint map acquisition submodule, used to perform dimensionality reduction sorting on the charge density distribution on the surface of each atom in the target molecule to obtain a fingerprint map of each atom in the target molecule.

[0014] Optionally, in an embodiment of the present application, the charge distribution acquisition submodule includes: a feature information extraction unit, used to extract structural feature information of the target molecule and energy feature information of the target molecule from the standard complex molecule file; a charge distribution determination unit, used to determine the total charge distribution of each atom in the target molecule based on the structural feature information and the energy feature information.

[0015] Optionally, in an embodiment of the present application, the atom category acquisition module includes: a fingerprint spectrum clustering submodule, which is used to cluster the fingerprint spectrum of each atom in the target molecule according to the charge density distribution of the atom.

[0016] Optionally, in an embodiment of the present application, the molecular force field includes: a potential energy equation between atomic clusters; a potential energy equation acquisition module, including: a probability distribution acquisition submodule, used to model different categories of atomic clusters and obtain the Boltzmann probability distribution between atoms in the atomic clusters; a potential energy equation determination submodule, used to determine the potential energy equation between atomic clusters based on the Boltzmann probability distribution between atoms in the atomic clusters.

[0017] Optionally, in an embodiment of the present application, the atomic cluster includes: an atomic pair consisting of two atoms, or an atomic group consisting of three atoms; the probability distribution acquisition submodule includes: a molecular atom division unit, used to divide all atoms in the target molecule into core area atoms and background area atoms; a background influence removal unit, used to iteratively remove the influence of background area atoms on the probability density distribution of core area atoms, and obtain the potential energy equation between atomic groups, or the potential energy equation between atomic pairs.

[0018] Optionally, in an embodiment of the present application, the molecular force field establishment module includes: an atomic cluster modeling submodule, which is used to model different categories of atomic clusters using atoms in the target molecule as nodes and potential energy equations between atomic clusters as edges between nodes to obtain a molecular force field.

[0019] Optionally, in an embodiment of the present application, the target molecules include: biological molecules and drug-like molecules.

[0020] An embodiment of the present application further provides an electronic device, including: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method described above is performed.

[0021] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described above is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of the flow of the molecular force field modeling method provided in an embodiment of the present application is shown;

[0024] Figure 2 A schematic diagram showing the mapping of the molecular structure of the atom pairs provided in the embodiment of the present application as the core region atoms;

[0025] Figure 3 The directed acyclic graphs of atomic pairs consisting of two atoms and atomic groups consisting of three atoms provided in the embodiments of the present application are shown;

[0026] Figure 4 A schematic diagram of a process of simplifying a molecular network using BFT provided in an embodiment of the present application is shown;

[0027] Figure 5 A schematic diagram of the structure of a molecular force field simulation device provided in an embodiment of the present application is shown;

[0028] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. It should be understood that the drawings in the embodiment of the present application only serve the purpose of explanation and description, and are not used to limit the protection scope of the embodiment of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in the embodiment of the present application shows the operation implemented according to some embodiments of the embodiment of the present application. It should be understood that the operation of the flowchart can be implemented out of order, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the embodiment of the present application, or remove one or more operations from the flowchart.

[0030] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The components of the embodiments of the present application described and shown in the drawings generally here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the embodiments of the present application claimed, but merely represents the selected embodiments of the embodiments of the present application.

[0031] It is understandable that the "first" and "second" in the embodiments of the present application are used to distinguish similar objects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not necessarily limit the difference. In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that there may be three relationships, such as A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the related objects before and after are in an "or" relationship. The term "multiple" refers to more than two (including two), and similarly, "multiple groups" refers to more than two groups (including two groups).

[0032] Before introducing the molecular force field modeling method provided in the embodiments of the present application, some concepts involved in the embodiments of the present application are first introduced:

[0033] Molecular force field refers to a physical mathematical model that describes the interactions within and between molecules. It is usually based on the potential function in classical physics to describe the relationship between relative positions and directions, so as to calculate the interaction forces between molecules. Molecular force field can be used to simulate, calculate and predict information on molecular structure, properties and dynamics, including conformation, vibration, thermodynamics, thermochemistry, spectrum and other aspects.

[0034] The structural characteristic information of molecules refers to the information required to determine the structure of a molecular system, which usually includes the position of atoms, the type and length of chemical bonds, the geometric configuration of molecules, etc. The structural characteristic information of molecules is the basis for molecular dynamics, simulation and calculation, and is crucial for understanding the structure, properties and functions of molecules.

[0035] The energy characteristic information of molecules refers to various information describing the energy of a molecular system, including total molecular energy, kinetic energy, potential energy, intramolecular interaction energy, etc. The energy characteristic information of molecules is the basis for understanding various chemical processes such as molecular stability, reaction kinetics, photochemical reactions and spectra.

[0036] Clustering refers to the process of dividing multiple character sequences into multiple classes consisting of similar character sequences according to one or more dimensions. In other words, the clusters generated by clustering are a collection of data objects that are similar to objects in the same cluster and different from objects in other clusters, which is what people often say, "Birds of a feather flock together."

[0037] Bayesian network is one of the most effective theoretical models in the field of uncertain knowledge expression and reasoning. Bayesian network uses directed edges to express the relationship between nodes, has stronger semantics, and provides a powerful tool for studying the conditional probability distribution of the geometric structure of target atomic pairs.

[0038] Boltzmann probability distribution is a mathematical model used to describe the distribution of particles in a thermodynamic system. It is based on the principles of statistical physics and is used to explain the distribution probability of particles at different energy levels. In a thermodynamic system, particles can be at different energy levels, each with a certain amount of energy. Boltzmann probability distribution tells us that the distribution probability of a particle at energy level i is related to the energy of that energy level and the temperature of the system.

[0039] It should be noted that the molecular force field modeling method provided in the embodiments of the present application can be executed by electronic devices, where the electronic devices refer to device terminals or servers that have the function of executing computer programs, such as smart phones, personal computers, tablet computers, personal digital assistants, or mobile Internet devices. Servers refer to devices that provide computing services through the network, such as x86 servers and non-x86 servers, and non-x86 servers include mainframes, minicomputers, and UNIX servers.

[0040] See also Figure 1 The schematic diagram of the process of the molecular force field modeling method provided by the embodiment of the present application is shown; the embodiment of the present application provides a molecular force field modeling method, including:

[0041] Step S110: establishing a fingerprint of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule.

[0042] The target molecule refers to the molecule for which the molecular force field needs to be modeled, and the target molecule may include: at least one of biological molecules and drug-like molecules; wherein the biological molecules and drug-like molecules here have a relative size relationship, and the biological molecules are larger than the drug-like molecules. Therefore, biological molecules are also called biological macromolecules, and drug-like molecules are also called drug-like small molecules.

[0043] The structural characteristic information of the above-mentioned target molecules can be obtained through multiple methods, such as experimental techniques such as X-ray crystallography, nuclear magnetic resonance, mass spectrometry, and computational methods based on quantum chemical calculations, molecular dynamics simulations, etc. These methods can be used to measure and calculate the geometric configuration, bond length, bond angle, electronic structure and other structural characteristic information of the molecules.

[0044] The energy characteristic information of the target molecule mentioned above can be obtained through a variety of calculation methods, among which the more commonly used ones include: quantum chemical calculation, classical molecular dynamics, molecular force field, etc. Through these calculation methods, the energy characteristic information of the molecule under a given environment can be calculated, including the total energy, vibration energy and internal molecular interaction energy of the molecule.

[0045] Step S120: clustering the fingerprints of each atom in the target molecule to obtain multiple atomic categories.

[0046] Fingerprints are used to identify each atom. Different atoms have different fingerprints, which can be used to identify and distinguish different atoms. The fingerprints here are mainly reflected and identified through different structural feature information and energy feature information.

[0047] It is understandable that since the target molecule may be a complex biological molecule with great diversity of physical and chemical properties, and the traditional molecular force field mostly defines the atomic categories based on artificial experience (i.e., classification is performed based on artificial experience), therefore, in order to eliminate the limitation of the traditional molecular force field in classifying atomic types based on artificial experience, machine learning methods (such as Bayesian networks) can be used to learn and classify the commonalities and differences of the high-dimensional charge density distribution within atoms, and the charge density can be regarded as one of the characteristic variables of the fingerprint map. In the specific practice, the K-means clustering algorithm (K-means) can be used to cluster the fingerprint map of each atom in the target molecule (such as a biological macromolecule system) to obtain multiple atomic categories.

[0048] Step S130: for each of the multiple atomic categories, modeling of atomic clusters of different categories is performed to obtain potential energy equations between the atomic clusters.

[0049] Since there are many types of atoms in the target molecule and the probability density distribution of atoms of each atomic category has an impact on the calculation of the potential energy equation, it is necessary to model the atomic clusters of each atomic category in order to obtain the potential energy equation between the atomic clusters of each atomic category.

[0050] Step S140: establishing a molecular force field according to potential energy equations of different types of atomic clusters.

[0051] In the process of implementing the above scheme, a fingerprint map of each atom in the target molecule is established according to the structural characteristic information and energy characteristic information of the target molecule, and the fingerprint map of each atom in the target molecule is clustered, so that the clustering process is effectively used to replace the classification of atom types based on human experience; the Bayesian method can also be used to establish a molecular graph model, conduct data mining, simulate multi-body energy, and establish a potential energy function, which effectively solves the problem that the current force field model based on Newtonian mechanics is not accurate enough, thereby reducing the dependence of the molecular force field on the fitting data, reducing the calculation time of the simulation process, and improving the accuracy of the simulated molecular force field.

[0052] As an optional implementation of the above step S110, the implementation of establishing the fingerprint of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule may include:

[0053] Step S111: Obtain the total charge distribution of all atoms in the target molecule.

[0054] All electrons on the surface of each atom in the target molecule can be obtained, and the total charge distribution of the atom can be calculated from all the electrons on the surface of each atom.

[0055] Step S112: Projecting the total charge distribution of all atoms in the target molecule onto the Fibonacci sphere lattice constructed for each atom to simulate the charge density distribution on the surface of each atom.

[0056] In the specific practice, the total charge distribution of each atom can be projected with charge density, so that the electrons of each atom can be projected onto the sphere to obtain the spherical points of each atom on the sphere. Then, an equidistantly distributed spherical point lattice (such as the Fibonacci point lattice of evenly distributed atoms) is constructed based on the spherical points of each atom on the sphere. Finally, the Gaussian distribution function is used to project the charge density of each spherical point, so as to construct the charge density distribution for each atom.

[0057] It is understandable that the charge density of an atom is mainly determined by the total charge of the protons at the nucleus of the atom and the electron density distribution around it, as well as the chemical environment in which the atom is located. The chemical environment in which the atom is located includes, for example, the types and geometric positions of other atoms around the atom in the molecular system, the way in which atoms are connected to each other, the relative geometric positions of each atom, etc., so the calculation of charge density is relatively complicated. In the specific practice, the above-mentioned charge density distribution can be calculated using quantum mechanical calculation methods, such as calculating the charge density information that converges to the space occupied by each atom in the molecular structure of protein molecules and drug-like compounds, and the charge density information includes the above-mentioned charge density distribution.

[0058] It is understandable that since the chemical environment in which the atoms are located is a kind of contextual environment, and the text corpus of natural language processing (NLP) is also a kind of contextual environment, the chemical environment in which the atoms are located is similar to the text corpus of NLP. Therefore, in the specific practice process, the distributed representation vector framework in the natural language processing algorithm can also be used to extract the charge density information of each atom in the three-dimensional structure of the biological molecule, and the charge density information includes the above-mentioned charge density distribution.

[0059] Step S113: performing dimensionality reduction sorting on the charge density distribution on the surface of each atom of the target molecule to obtain a fingerprint of each atom in the target molecule.

[0060] An implementation method of the above step S113 is, for example: using the equation form of the Gaussian basis group to project the charge of each atom in the molecule onto the atomic sphere, obtaining the charge density of each atom at the spherical point on the sphere, and performing dimensionality reduction sorting on the charge density distribution on the surface of each atom, thereby obtaining a fingerprint map of each atom.

[0061] As a first optional implementation of the above step S111, the implementation of determining the total charge distribution of atoms according to the structural feature information and energy feature information extracted from the standard complex molecule file may include:

[0062] Step S111a: extracting the structural characteristic information and the energy characteristic information of the target molecule from the standard complex molecule file.

[0063] The above-mentioned standard complex molecule file may include the structure file of biological molecules (such as protein structure) and the structure file of drug-like small molecules; before extracting the structural characteristic information of the target molecule and the energy characteristic information of the target molecule, the biological molecule (such as protein atom) file and the drug-like small molecule structure file may also be subjected to structural correction and structural optimization, for example: the structural characteristic information, energy characteristic information and charge density information (charge density information includes charge density distribution) of proteins and drug-like small molecules may be obtained, the 6-31G* basis set and Restrained ElectroStatic Potential (RESP) may be used to fit and generate the atomic charge information of proteins, and the atomic charge information of drug-like small molecules may be obtained by calculating the am1-bcc charge.

[0064] Step S111b: Determine the total charge distribution of each atom in the target molecule according to the structural characteristic information and the energy characteristic information.

[0065] The total charge distribution refers to the total amount of information characterizing the charge distribution of an atom.

[0066] As a second optional implementation of the above-mentioned step S111, the total charge distribution of atoms can be determined based on the structural characteristic information and energy characteristic information extracted from the database. For example, the structural characteristic information and energy characteristic information of the target molecule can be extracted from the protein database, and the structural characteristic information and energy characteristic information of the target molecule can also be extracted from the drug-like small molecule database, and the total charge distribution of each atom in the target molecule can be determined based on the structural characteristic information and energy characteristic information.

[0067] As an optional implementation of the above step S120, the fingerprint spectrum of each atom in the target molecule can be clustered using a machine learning method. For example, a K-means clustering algorithm (K-means clustering algorithm, K-means) is used to cluster and classify the atoms of each atomic category in different atomic categories in the target molecule according to the charge density distribution of the atoms, so as to obtain multiple atomic categories; wherein, the silhouette coefficient method can be used for evaluation, and the classification number and centroid coordinates with the highest score are retained, so as to define the atomic type for each element. Optionally, after clustering and classifying the atoms of each atomic category in different atomic categories in the target molecule according to the charge density distribution of the atoms, the clustering results of multiple atomic categories are obtained. In the specific practice process, the clustering evaluation method can also be used to evaluate the above clustering results, so as to finally obtain the classification standard for classifying atomic categories. After obtaining the classification standard, the electronic device can directly use the classification standard to classify atoms according to the charge density distribution of the atoms.

[0068] As an optional implementation of the above step S130, the above molecular force field may include: a potential energy equation between atomic clusters; the implementation of modeling different types of atomic clusters may include:

[0069] Step S131: Modeling the atomic cluster to obtain the Boltzmann probability distribution between atoms in the atomic cluster.

[0070] It can be understood that in the process, all atoms in the target molecule can be divided into core area atoms and background area atoms, and the influence of the background area atoms on the probability density distribution of the core area atoms can be removed in an iterative manner, and finally the atoms corresponding to the atomic type selected in the core area each time are obtained, and the Boltzmann probability distribution between the atoms of the atomic cluster is formed under the action of the potential energy inside the selected atomic type.

[0071] Step S132: determining the potential energy equation between the atomic clusters according to the Boltzmann probability distribution between the atoms in the atomic clusters.

[0072] The implementation of the above step S132 is as follows: It can be understood that the conformational potential energy of N atoms corresponding to the N selected atomic types can be derived from the Boltzmann probability, so the formula E can be used. N (R N )=-RTln[P(R N ) calculates the Boltzmann probability distribution between atoms in the atomic cluster to obtain the potential energy equation between the atomic clusters; where N represents the number of atomic categories selected in the core area, R N Represents the conformational coordinate variables of N atoms, E N Indicates that the selected N atoms have a conformational coordinate variable R N When N = 2, the above formula calculates the potential energy equation of the atomic pair consisting of two atoms. Similarly, when N = 3, the above formula calculates the potential energy equation of the atomic group consisting of three atoms. When N can be greater than 3, E N Indicates that the selected N atoms have a conformational coordinate variable R N The potential energy equation of the system when .

[0073] As an optional implementation of the above step S131, the above atomic cluster may include an atomic pair consisting of two atoms, or the above atomic cluster may include an atomic group consisting of three atoms.

[0074] See also Figure 2 The schematic diagram of the mapping of the molecular structure of the atom pair provided by the embodiment of the present application as the core region atom is shown; for ease of understanding and explanation, the following is a detailed description of the atom pair composed of two atoms as the core region atom, and a traversal method can be used to select all atoms corresponding to the specific N types of atoms in the target molecule and place them in the core region (such as the inner area of ​​the dotted ellipse in the figure), and establish a molecular force field for the N types of atoms. The above-mentioned implementation methods for modeling different types of atomic clusters may include:

[0075] Step S133: Divide all atoms in the target molecule into core region atoms and background region atoms.

[0076] It can be understood that the core atoms are the atoms for which the molecular force field needs to be established, such as the atoms in the inner area of ​​the dotted ellipse in the figure. i and r j Two atoms, background atoms refer to other atoms that have an impact on the probability density distribution of the core atoms, such as other atoms in the dotted ellipse outer area in the figure (i.e., except r i and r j atoms other than the two atoms).

[0077] Step S134: removing the influence of the background region atoms on the probability density distribution of the core region atoms in an iterative manner, and obtaining the potential energy equation between the atomic groups, or the potential energy equation between the atomic pairs.

[0078] In the specific practice, each target molecule in the structural database containing protein-ligand structure is subjected to data mining to obtain the probability density distribution of the core region atoms, and then the influence of the background region atoms on the probability density distribution of the core region atoms is removed by iteration to obtain the potential energy equation between the atom pairs. Similarly, the potential energy equation between the atom groups is also obtained by the above method. For example, each target molecule in the structural database containing protein-ligand structure is subjected to data mining to obtain the probability density distribution of the core region atoms, and then the influence of the background region atoms on the probability density distribution of the core region atoms is removed by iteration to obtain the potential energy equation between the atom groups.

[0079] As an optional implementation of the above step S140, the implementation of establishing the molecular force field according to the potential energy equations of different types of atomic clusters may include:

[0080] Step S141: Taking the atoms in the target molecule as nodes and the potential energy equations between the atomic clusters as the edges between the nodes, modeling different types of atomic clusters is performed to obtain a molecular force field.

[0081] It is understandable that a Bayesian network is a directed acyclic graph (DAG), and a Bayesian network describes a causal chain through directed edges (conditional probabilities express relationship strength). In any Bayesian network, a cause can produce a result, and the result can also become a priori condition for other causes in a causal chain, and the same cause will not become a priori condition for itself. In any target molecule system, there is an interaction potential between every two atoms, which makes the DAG graph of a molecule always able to be represented by a fully connected network graph model. It is precisely because the Bayesian network has the above characteristics that the Bayesian network can be used to model different types of atomic clusters, that is, to use the Bayesian network to represent different molecular structures, for example: the atoms in the target molecule are nodes (that is, the nodes in the DAG graph are used to represent atoms), the potential energy equations between atomic clusters are the edges between nodes (representing the energy transfer relationship between atomic interactions), and the Bayesian network is used to model different types of atomic clusters to obtain molecular force fields.

[0082] See also Figure 3The directed acyclic graph of an atom pair consisting of two atoms and an atom group consisting of three atoms provided in the embodiment of the present application is shown; the arrowed lines between the nodes in the figure represent the interaction relationship and direction. In order to better describe the process of modeling the Boltzmann probability of an atom pair ij using DAG, the atomic coordinate node r of the atom pair ij can be i and r j A node c is added in the middle ij To describe whether atoms i and j interact with each other.

[0083] The likelihood probability can be obtained from the Bayesian probability formula: Among them, P(c ij | i , r j ) represents the likelihood probability between atom pair ij, P(c ij ) is the prior probability of whether the two atoms in the atom pair ij interact with each other. When the atom pair ij interacts with each other, the prior probability is 1, and when the atom pair ij does not interact with each other, the prior probability is 0. ij ) represents the unconditional probability distribution of atom pair ij without constraints.

[0084] from Figure 2 It can be seen that all atoms in the molecule can reach the target atom pair through a certain path. According to the chain rule chain of probability, all nodes on these chains can interact with the target atom pair through a certain path. The following formula can be used to describe this chain.

[0085]

[0086] Where C is the set of all pairwise interactions in the target molecular system, D is the set of all atom pairs at different distances in the target molecular system, R is the set of all atomic coordinate nodes in the target molecular system, and v is the sequence number of the atomic distance node or interaction node closest to the target atomic pair. ij | ij ) is the Boltzmann probability of the target atom pair, that is, the radial distribution of the target atom pair affected only by the interaction between them.

[0087] In a molecular network, each atom can be in multiple interactions, and different types of atoms can be connected in different ways. Since the interaction between target atom pairs cannot be quantified individually, using such a complex structure to train the model will produce the "curse of dimensionality". The above Bayesian network can generate the Boltzmann probability P (dij | ij ). In molecular networks, the true potential energy of atoms is difficult to measure directly, however, the distance between atoms can be measured directly. Therefore, the true potential energy of atoms can be modeled based on the distance between atoms, and it is considered that the farther the node is from the target node, the smaller the influence. In order to correct the molecular network, the Bayesian Field Theory (BFT) can be used to assume that the background interaction chain is a Markov chain, and each node is determined only by its previous node. The above BFT introduces background nodes to represent the indirect interactions between neighboring atoms that are farther away from the target atom. In the corrected molecular graph, each background interaction chain is separated and modeled using a method based on Bayesian theory.

[0088] See also Figure 4 The schematic diagram of the process of using BFT to simplify the molecular network provided by the embodiment of the present application is shown; the figure shows the process of simplifying the target molecule using BFT, and sub-graph A represents the interaction relationship between the atom r and the collar atom a except the collar atom, wherein the interaction relationship is represented by node c. Sub-graph B represents the schematic diagram when the interatomic distance node dij is used to replace the target atom coordinate nodes ri and rj, and sub-graph C represents the use of background node b to describe the entire background area, and each background node can be represented by a Markov chain. The action between the nodes, the Markov chain here is, for example: first from the background node b to the collar atom a, and then from the collar atom a to the target atom pair. When the multi-body effect and the correlation between the background nodes are not considered, in the directed graph, the multi-conditional probability simplified by BFT can be extended according to the chain rule of probability, including the probability of all chain nodes tracing back along each path until reaching the end or collision node (node ​​pointed to by multiple arrows). In the molecular network optimized by BFT, the conditional probability of the target atom pair composed of any two atoms can be expressed as follows:

[0089]

[0090] Among them, m represents the sequence number of the action path, N v Indicates the total number of action paths.

[0091] Similarly, in the molecular network after BFT optimization, the conditional probability of the target atomic group composed of any three atoms can be expressed as follows:

[0092]

[0093] Among them, m represents the sequence number of the action path, N v Indicates the total number of action paths.

[0094] In the specific practice, statistical methods can be used to calculate the conditional probability between the neighboring atoms and the target atom or target structure in each action path. The conditional probability here can be expressed as follows:

[0095]

[0096] Among them, P(d m |c m )P(d ij | m )P(d m |b m ) represents the influence of the mth action path on the target atom pair or target triatomic structure (also known as an atomic group composed of three atoms). By iteratively deriving the above formula, we can eliminate the influence of the probability density distribution of the background atoms on the core atoms, thereby obtaining the interaction probability distribution of the independent target structure in each action path that is only affected by the change in potential energy. The two-atom Boltzmann probability distribution of the potential energy between the above atomic pairs can be expressed as:

[0097]

[0098] Among them, P(d m |c m )P(d ij | m )P(d m |b m ) represents the influence of the mth action path on the target atom pair or target triatomic structure (also known as an atomic group composed of three atoms). The above two formulas can be used to derive the conditional probability distribution of the triatomic structure changing with potential energy when there is a multi-body effect:

[0099]

[0100] Among them, P(d m |c m )P(d ij | m )P(d m |b m ) represents the influence of the mth action path on the target atom pair or target triatomic structure (also known as an atomic group composed of three atoms).

[0101] It is understandable that the specific meanings of the letter variables not explained in the above formulas can be found in the definition table of variable symbols in Table 1 below, and the specific meanings of the letter variables between formulas can also be referenced to each other.

[0102]

[0103]

[0104] Table 1 Variable symbol definition table

[0105] See also Figure 5 The structure diagram of the molecular force field simulation device provided in the embodiment of the present application is shown; the embodiment of the present application provides a molecular force field simulation device 200, including:

[0106] The fingerprint spectrum establishment module 210 is used to establish a fingerprint spectrum of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule.

[0107] The atom category acquisition module 220 is used to cluster the fingerprint spectrum of each atom in the target molecule to obtain multiple atom categories.

[0108] The potential energy equation obtaining module 230 is used to model different types of atomic clusters for each atomic type in the multiple atomic types, and obtain potential energy equations between the atomic clusters.

[0109] The molecular force field establishment module 240 is used to establish a molecular force field according to potential energy equations of different types of atomic clusters.

[0110] Optionally, in an embodiment of the present application, the fingerprint spectrum establishment module includes:

[0111] The charge distribution acquisition submodule is used to obtain the total charge distribution of all atoms in the target molecule.

[0112] The charge density simulation submodule is used to project the total charge distribution of all atoms in the target molecule onto the Fibonacci sphere lattice constructed for each atom to simulate the charge density distribution on the surface of each atom.

[0113] The fingerprint acquisition submodule is used to reduce the dimension of the charge density distribution on the surface of each atom in the target molecule and obtain the fingerprint of each atom in the target molecule.

[0114] Optionally, in an embodiment of the present application, the charge distribution acquisition submodule includes:

[0115] The characteristic information extraction unit is used to extract the structural characteristic information and the energy characteristic information of the target molecule from the standard complex molecule file.

[0116] The charge distribution determination unit is used to determine the total charge distribution of each atom in the target molecule according to the structural characteristic information and the energy characteristic information.

[0117] Optionally, in the embodiment of the present application, the atom category obtaining module includes:

[0118] The fingerprint clustering submodule is used to cluster the fingerprint of each atom in the target molecule according to the charge density distribution of the atom.

[0119] Optionally, in an embodiment of the present application, the molecular force field includes: a potential energy equation between atomic clusters; a potential energy equation acquisition module, including:

[0120] The probability distribution acquisition submodule is used to model different categories of atomic clusters and obtain the Boltzmann probability distribution between atoms in the atomic clusters.

[0121] The potential energy equation determination submodule is used to determine the potential energy equation between atomic clusters according to the Boltzmann probability distribution between atoms in the atomic clusters.

[0122] Optionally, in the embodiment of the present application, the atomic cluster includes: an atomic pair consisting of two atoms, or an atomic group consisting of three atoms; the probability distribution acquisition submodule includes:

[0123] The molecular atom division unit is used to divide all atoms in the target molecule into core region atoms and background region atoms.

[0124] The background influence removal unit is used to remove the influence of the background area atoms on the probability density distribution of the core area atoms in an iterative manner, and obtain the potential energy equation between the atomic groups, or the potential energy equation between the atomic pairs.

[0125] Optionally, in an embodiment of the present application, the molecular force field establishment module includes:

[0126] The atomic cluster modeling submodule is used to model different types of atomic clusters using atoms in the target molecule as nodes and potential energy equations between atomic clusters as edges between nodes to obtain molecular force fields.

[0127] Optionally, in an embodiment of the present application, the target molecules include: biological molecules and drug-like molecules.

[0128] It should be understood that the device corresponds to the above-mentioned molecular force field modeling method embodiment and can perform the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be referred to in the above description, and the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0129] See also Figure 6The electronic device 300 provided in the embodiment of the present application includes: a processor 310 and a memory 320, wherein the memory 320 stores machine-readable instructions executable by the processor 310, and when the machine-readable instructions are executed by the processor 310, the above method is executed.

[0130] The embodiment of the present application further provides a computer-readable storage medium 330 , on which a computer program is stored. When the computer program is executed by the processor 310 , the above method is executed.

[0131] Among them, the computer-readable storage medium 330 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0132] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device embodiments described above are only schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a part of a module, a program segment or a code, and a part of a module, a program segment or a code includes one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also be different from the order of occurrence marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which is mainly determined by the functions involved.

[0134] In addition, each functional module of each embodiment in the embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. In addition, in the description of this specification, the description of reference terms "one embodiment", "some embodiments", "example", "specific example", "some examples", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present application. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradiction.

[0135] The above description is only an optional implementation manner of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the embodiments of the present application, which should be covered within the protection scope of the embodiments of the present application.

Claims

1. A molecular force field modeling method, characterized in that: include: Establishing a fingerprint spectrum of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule; Clustering the fingerprints of each atom in the target molecule to obtain multiple atomic categories; For each of the multiple atomic categories, modeling atomic clusters of different categories to obtain potential energy equations between the atomic clusters; The molecular force field is established according to the potential energy equations of the different types of atomic clusters.

2. The method according to claim 1, characterized in that The step of establishing a fingerprint spectrum of each atom in the target molecule according to the structural characteristic information and the energy characteristic information of the target molecule comprises: Obtaining the total charge distribution of all atoms in the target molecule; Projecting the total charge distribution of all atoms in the target molecule onto the Fibonacci sphere lattice constructed for each atom to simulate the charge density distribution on the surface of each atom; The charge density distribution on the surface of each atom of the target molecule is sorted by dimensionality reduction to obtain a fingerprint of each atom in the target molecule.

3. The method according to claim 2, characterized in that The obtaining the total charge distribution of all atoms in the target molecule comprises: Extracting structural characteristic information of the target molecule and energy characteristic information of the target molecule from the standard complex molecule file; The total amount of charge distribution of each atom in the target molecule is determined according to the structural characteristic information and the energy characteristic information.

4. The method according to claim 1, characterized in that: The clustering of the fingerprint spectrum of each atom in the target molecule comprises: The fingerprint spectrum of each atom in the target molecule is clustered according to the charge density distribution of the atom.

5. The method according to claim 1, characterized in that The molecular force field includes: potential energy equations between atomic clusters; the modeling of different types of atomic clusters includes: Modeling the atomic cluster to obtain a Boltzmann probability distribution between atoms in the atomic cluster; The potential energy equation between the atomic clusters is determined according to the Boltzmann probability distribution between the atoms of the atomic clusters.

6. The method according to claim 1, characterized in that The atomic cluster includes: an atomic pair consisting of two atoms, or an atomic group consisting of three atoms; the modeling of different types of atomic clusters includes: Dividing all atoms in the target molecule into core region atoms and background region atoms; The influence of the background region atoms on the probability density distribution of the core region atoms is removed in an iterative manner, so as to obtain a potential energy equation between the atom groups or a potential energy equation between the atom pairs.

7. The method according to claim 1, characterized in that The step of establishing the molecular force field according to the potential energy equations of the different types of atomic clusters comprises: The atoms in the target molecule are used as nodes, the potential energy equations between the atomic clusters are used as edges between the nodes, and the atomic clusters of different categories are modeled to obtain the molecular force field.

8. The method according to any one of claims 1 to 7, characterized in that: The target molecules include: biological molecules and drug-like molecules.

9. A molecular force field simulation device, characterized in that: include: A fingerprint spectrum establishment module is used to establish a fingerprint spectrum of each atom in the target molecule according to the structural characteristic information and energy characteristic information of the target molecule; An atomic category acquisition module, used for clustering the fingerprint spectrum of each atom in the target molecule to obtain multiple atomic categories; A potential energy equation obtaining module is used to model different types of atomic clusters for each type of atomic category in the multiple types of atomic categories, and obtain potential energy equations between the atomic clusters; The molecular force field establishment module is used to establish the molecular force field according to the potential energy equations of the atomic clusters of different categories.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method according to any one of claims 1 to 8 is performed.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is executed.

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