Rapid verification system and method for docking and RMSD optimization

Abstract

The invention discloses a quick verification system and method for docking and RMSD optimization applied to the field of biomedical computational simulation, and the method comprises the steps: constructing a multi-stage screening and error compensation mechanism of a molecular conformation, controlling the computational complexity through employing a discretized grid in an initial sampling stage, compressing a candidate space through employing a quick energy function in a coarse screening stage, and carrying out the quick verification of the RMSD optimization. Gradient-driven local optimization is introduced in the refinement stage to improve the structural precision, a residual error correction module based on a neural network is deployed in the final stage to carry out nonlinear correction on systematic coordinate deviation, a closed-loop optimization process is formed, and on the premise that the process does not depend on quantum mechanical calculation with high calculation cost or long-time molecular dynamics simulation, the precision of the system is greatly improved. The root-mean-square deviation of the docking conformation is stably controlled within 0.5 angstroms, and meanwhile, the single molecule processing time is kept at the order of fifty milliseconds, and the speed is increased by more than 20 times compared with a traditional high-precision docking method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological and medical computing simulation, in particular to a rapid verification system and method for docking and RMSD optimization. BACKGROUND

[0002] With the deep application of computer-aided drug design in target discovery, lead compound optimization and virtual screening, etc., the performance of molecular docking technology as the core computing engine directly determines the research and development efficiency and the reliability of the results. In the development of new eye drug delivery systems such as drug-loaded contact lenses (DLCLs), this technology is particularly important - DLCLs aims to achieve long-acting and precise release of drugs on the corneal surface by combining drugs with contact lenses, thereby overcoming the defects of traditional eye drops such as low bioavailability and frequent dosing; and the key to "what drugs to deliver" is highly dependent on the efficient generation and screening of candidate drug molecules, i.e. the discovery and optimization process of drug molecules.

[0003] Traditional molecular docking methods rely on a single scoring function and a fixed sampling strategy, and ligand conformation searching is performed through a pre-set energy threshold and a spatial grid, and its theoretical basis is based on the rigid receptor assumption and static scoring model. However, biological macromolecules actually exhibit significant conformational flexibility during ligand binding: side chain rearrangement, main chain micro-motion and solvation effect of the active pocket will dynamically adjust with the ligand structure. The static docking strategy is prone to cause false positive conformation accumulation, misjudgment of key hydrogen bond network or failure of hydrophobic cavity filling due to its inability to capture such receptor-induced fitting behavior, thereby exacerbating systematic bias of scoring function and conformational space sampling redundancy and other structural problems, significantly reducing the enrichment factor of virtual screening. In addition, in the face of the increasing demand for computing throughput, result consistency and explainability of industrial-level high-throughput screening (such as rapid iteration of lead compounds, multi-target cross-validation, preclinical toxicity prediction), the closed scoring system and rigid sampling framework of traditional single-engine methods have been difficult to meet the demand for accuracy and efficiency optimization in complex pharmacodynamic scenarios.

[0004] Therefore, while developing a docking strategy that is more suitable for flexible receptor simulation and dynamic binding evaluation, there is an urgent need for an auxiliary means that can quickly verify and optimize the RMSD of the docking results to improve the reliability of the screening results, and thus promote the efficient design and development of drug molecules in precise drug delivery systems such as drug-loaded contact lenses. SUMMARY

[0005] The core of the application is to construct a set of verification process which combines local geometric invariant feature vector, residual neural network correction, multi-stage coarse and fine collaborative screening and backtracking control mechanism, to solve the technical contradiction that the calculation accuracy and operation efficiency are difficult to be considered in the existing molecular docking process, and to overcome the problem of high misjudgment rate caused by conformation noise and local geometric distortion in the traditional RMSD structure deviation evaluation method in the high-throughput screening scene.

[0006] To solve the above problems, the application adopts the following technical scheme.

[0007] The rapid verification method for docking and RMSD optimization includes the following operations: Step S1, performing initial space posture traversal on the ligand molecule through a molecular conformation sampling engine module: traversing a discretized torsion angle grid divided based on the rotational bond degree of freedom, each grid node corresponding to an independent conformation instance, the number of conformation instances being determined by a preset sampling density parameter; Step S2, inputting the conformation instance into a coarse-grained scoring function module, the coarse-grained scoring function module adopting a fast energy estimation model based on the superposition of van der Waals potential and electrostatic potential, performing spatial fitness evaluation in the receptor binding pocket for each conformation instance, and retaining a candidate conformation set with a score value in the top 5 percent; Step S3, performing local geometric refinement operation on the candidate conformation set, the local geometric refinement operation including three rounds of iterative optimization, in each round of iteration, applying a spatial displacement correction based on gradient descent to the non-bonding atoms of the ligand molecule, the displacement step being dynamically adjusted by a convergence factor of the current round, the initial value of the convergence factor being 0.2 angstrom, and decreasing by 20 percent each round; Step S4, after completing the local geometric refinement, starting a residual structure deviation detection module, extracting all pairs of atoms connected by rotatable bonds in the ligand molecule, calculating the change vector of the three-dimensional coordinates before and after refinement, and constructing a local geometric invariant feature vector; Step S5, inputting the local geometric invariant feature vector into the residual correction neural network for training; Step S6, superimposing the coordinate correction amount output by the residual correction neural network on the atomic coordinates of the refined conformation to generate a final optimized conformation; Step S7, after generating the final optimized conformation, performing a structure consistency verification process, calculating the root mean square deviation between the final optimized conformation and the original crystal conformation, if the root mean square deviation is greater than 0.5 angstrom, triggering a backtracking mechanism, selecting a suboptimal conformation instance from the candidate conformation set to re-execute the local geometric refinement and residual correction process, until the root mean square deviation meets the threshold requirement or the candidate conformation set is exhausted.

[0008] Further, the specific operation of performing initial spatial pose traversal on the ligand molecule in step S1 includes the following: identifying the single bond connection relationship in the molecular topology, excluding the intra-ring bond and the terminal methyl rotation, and only performing torsion angle discretization processing on the single bond connecting two non-hydrogen atoms and not participating in the aromatic ring system; generating independent conformation instances within the preset torsion angle grid range according to the sampling density parameter of three to seven discrete angle values per degree of freedom; controlling the total number of conformation instances not to exceed the upper limit of computing resources, and for ligand molecules containing more than ten rotatable bonds, starting the hierarchical sampling strategy, first performing full grid sampling on the main chain rotatable bonds, and then performing fixed one hundred random sampling on the side chain rotatable bonds.

[0009] Further, the specific operation of performing spatial fitness evaluation on each conformation instance in step S2 includes the following: calculating the van der Waals potential in the form of a combination of a twelve power repulsive term and a six power attractive term; calculating the electrostatic potential according to Coulomb's law, with the dielectric constant set to four and the atomic charge using the pre-allocated molecular force field charge value; sorting the evaluation results, retaining the candidate conformation set with the top five percent of score values, and additionally forcibly retaining the three conformation instances with the highest scores in the original conformation.

[0010] Further, the specific operation of performing local geometry refinement on the candidate conformation set in step S3 includes the following: using a gradient descent algorithm with momentum acceleration strategy, with the momentum coefficient set to zero point nine, the initial learning rate set to zero point zero one, and the learning rate multiplied by zero point nine five after each iteration to decay; applying spatial displacement correction to non-bonding atoms in each iteration, with the displacement step length dynamically adjusted by the convergence factor of the current iteration; if the total energy change of the conformation of the last two iterations is less than zero point zero zero one electron volts, the optimization process of the current conformation is terminated in advance and marked as a converged state.

[0011] Further, the specific operation of constructing the local geometric invariant feature vector in step S4 includes the following: extracting all pairs of atoms connected by rotatable bonds in the ligand molecule; calculating the change vector of the three-dimensional coordinates before and after refinement; defining the bond length change as the absolute value of the difference between the refined bond length and the pre-refinement bond length; defining the bond angle change as the absolute value of the difference between the refined bond angle and the pre-refinement bond angle; defining the dihedral angle change as the absolute value of the difference between the refined dihedral angle and the pre-refinement dihedral angle; all angle quantities are expressed in radians, and are concatenated to form a three-dimensional local geometric invariant feature vector.

[0012] Further, the residual correction neural network training operation in step S5 includes the following: screening ligand-protein complex crystal structures with a resolution less than or equal to 0.8 angstrom from a protein database; using the same docking software and parameters to generate the corresponding calculated conformation; calculating the three-dimensional coordinate offset between the calculated conformation and the crystal conformation for each atom; extracting the geometric invariant features of the local chemical environment in which the atom is located; taking the geometric invariant features as input and the coordinate offset as output to form a training sample pair; using the mean square error loss function, the adaptive moment estimation algorithm optimizer, setting the batch size to thirty-two, the training rounds to five hundred, saving a model snapshot every fifty rounds, and finally selecting the snapshot with the smallest validation set loss as the deployment model.

[0013] Further, the specific operation of generating the final optimized conformation in step S6 includes the following: limiting the amplitude of the correction output by the residual correction neural network, the modulus of the single-atom coordinate correction should not exceed 0.3 angstrom; the excess part is compressed to the threshold range in proportion; superimposing to generate the final optimized conformation for subsequent use in free energy calculation and pharmacophore matching analysis.

[0014] Further, the specific operation of executing the structure consistency verification process in step S7 includes the following: calculating the root mean square deviation between the final optimized conformation and the original crystal conformation, only for heavy atoms in the ligand molecule; performing a rigid superposition operation before calculation, and the superposition target is to minimize the root mean square deviation of the heavy atom coordinate set; if any bond length deviates from the standard value by more than fifteen percent or any bond angle deviates from the standard value by more than twenty degrees, automatically trigger the local structure re-optimization subprogram, and use the conjugate gradient method to optimize only the geometric parameters of the abnormal bond or angle under the condition of fixing the coordinates of the remaining atoms.

[0015] Further, the operation when the backtracking mechanism is started in step S7 includes the following: selecting the next unprocessed conformation instance in order from the candidate conformation set according to the original score value order; re-executing the local geometry refinement and residual correction process; the maximum number of retries is set to three; after three retries fail, save the original docking conformation and the three optimized conformations to the abnormal case database, record the chemical identifier and the failure reason code; at the same time, start the model fine-tuning process, when the number of abnormal cases accumulates to more than one hundred, use the new case data to perform ten rounds of incremental training on the existing network weights, set the learning rate to one tenth of the original training learning rate, and replace the original model file after training is completed.

[0016] Further, the following steps are also included: during the process of steps S1 to S5, the intermediate calculation results of the processed conformation instances are stored by hash indexing through the data cache layer, and when the same conformation appears again, the cache result is directly read.

[0017] The rapid verification system of docking and RMSD optimization includes the following modules: a molecular conformation sampling engine module for performing initial spatial pose traversal on a ligand molecule; a coarse-grained scoring function module for performing spatial fitness evaluation on each conformation instance; a local geometry refinement module for performing three rounds of iterative optimization on a candidate conformation set; a residual structure bias detection module for constructing a local geometry invariant feature vector; a residual correction neural network module for receiving the local geometry invariant feature vector as input and outputting a three-dimensional coordinate correction amount through a three-layer fully connected structure, supporting dynamic incremental training, when the abnormal case database accumulates more than one hundred new cases, automatically starting a ten-round incremental training process, with a learning rate set to one-tenth of the original training learning rate, and replacing the original model file after training is completed; a coordinate superposition module for superimposing the coordinate correction amount output by the residual correction neural network on the atomic coordinates of the refined conformation to generate a final optimized conformation; a structure consistency verification module for calculating the root mean square deviation between the final optimized conformation and the original crystal conformation; a backtracking control module for selecting a score suboptimal conformation instance from the candidate conformation set to re-execute the local geometry refinement and residual correction process.

[0018] Further, the molecular conformation sampling engine module, the coarse-grained scoring function module, the local geometry refinement module, the residual structure bias detection module, the residual correction neural network module, the coordinate superposition module, the structure consistency verification module and the backtracking control module are all deployed in a unified computing framework, data is transmitted between multiple modules through a shared memory pool, and multi-thread parallel processing is supported.

[0019] Compared with the prior art, the application has the following advantages: The present scheme constructs a multi-stage screening and error compensation mechanism for molecular conformation, uses a discretized grid to control the computational complexity in the initial sampling stage, uses a fast energy function to compress the candidate space in the coarse screening stage, introduces a gradient-driven local optimization to improve the structure precision in the refinement stage, and deploys a neural network-based residual correction module to correct the systematic coordinate bias in the final stage, forming a closed-loop optimization process. Without relying on high computational cost quantum mechanics calculation or long-time molecular dynamics simulation, the root mean square deviation of the docking conformation is stably controlled within 0.5 angstrom, and the single molecule processing time is maintained at the order of 50 milliseconds, which is more than 20 times faster than traditional high-precision docking methods. The introduction of structure consistency verification and backtracking mechanism ensures the robustness of the results, and the automatic filing of abnormal cases and the model incremental training function enable the system to continuously evolve. The system can be seamlessly integrated into existing virtual screening platforms, significantly improving the accuracy of lead compound identification and research and development efficiency, reducing false positive screening results caused by structural bias, and shortening the drug discovery cycle. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The system architecture of the present application is shown in the figure; Figure 2 The core principle framework of the residual correction neural network based on local geometric invariants of the present application is shown in the figure; Figure 3 The three progressive processing levels of the backtracking mechanism of the present application are shown in the figure; Figure 4 The system deployment framework of the memory sharing and multi-thread parallel computing architecture among multiple modules of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] The technical solutions will be described in detail below with reference to the drawings in the embodiments of the present application.

[0022] Embodiment:

[0023] Please refer to Figures 1-4 , the rapid verification system for docking and RMSD optimization includes the following modules: Molecular conformation sampling engine module, for performing initial spatial pose traversal on ligand molecules; Coarse-grained scoring function module, for performing spatial fitness evaluation on each conformation instance; Local geometry refinement module, for performing three rounds of iterative optimization on the candidate conformation set; Residual structure bias detection module, for constructing a local geometric invariant feature vector; Residual correction neural network module, for receiving the local geometric invariant feature vector as input, and outputting a three-dimensional coordinate correction amount through a three-layer fully connected structure, supporting dynamic incremental training. When the abnormal case database accumulates more than one hundred new cases, ten rounds of incremental training process are automatically started, the learning rate is set to one tenth of the original training learning rate, and the original model file is replaced after training is completed; Coordinate superposition module, for superimposing the coordinate correction amount output by the residual correction neural network on the atomic coordinates of the refined conformation to generate the final optimized conformation; Structure consistency verification module, for calculating the root mean square deviation between the final optimized conformation and the original crystal conformation; A backtracking control module is configured to re-execute the local geometry refinement and residual correction procedure from the candidate conformation set with the second-best scored conformation instance.

[0024] The molecular conformation sampling engine module, the coarse-grained scoring function module, the local geometry refinement module, the residual structure bias detection module, the residual correction neural network module, the coordinate superposition module, the structure consistency verification module, and the backtracking control module are all deployed in a unified computing framework. Data is transmitted between the modules through a shared memory pool to avoid disk read-write delays. The entire system supports multi-thread parallel processing, and the maximum number of concurrent threads is determined by the number of physical cores.

[0025] The rapid verification method for docking and RMSD optimization includes the following operations: Step S1, perform initial spatial pose traversal on the ligand molecule by the molecular conformation sampling engine module: traverse the discretized torsion angle grid based on the rotation bond degree of freedom, each grid node corresponds to an independent conformation instance, and the number of conformation instances is determined by a preset sampling density parameter, the sampling density parameter takes a value in the range of three to seven discrete angle values per degree of freedom; Perform initial conformation space exploration based on Monte Carlo tree search on the input candidate ligand molecule by the molecular conformation sampling engine module to generate a candidate conformation set covering the low-energy state region; In this step, the molecular conformation sampling engine module has a built-in three-dimensional rotation bond angle degree of freedom resolver, which divides the rotatable bonds in the molecule into rigid core fragments and flexible side chain fragments according to the topological level. The rigid core fragments are sampled using a fixed backbone sampling strategy, which only allows their overall spatial translation and rotation. The flexible side chain fragments are enabled with an incremental dihedral angle perturbation mechanism to perform random walk within a range of ±180 degrees with a preset step size. After each perturbation, the molecular force field energy evaluation unit is called to calculate the sum of van der Waals potential, electrostatic potential, and solvation free energy of the current conformation. If the total energy is lower than the threshold of the highest energy conformation in the current conformation pool, the conformation is included in the candidate set, and a local minimum escape algorithm is triggered to force the current optimal conformation out of the local energy valley by introducing Gaussian noise to perturb the dihedral angle parameters. The conformation sampling process continues until five hundred consecutive perturbations do not produce a new conformation with lower energy, or the total sampling time reaches the preset upper limit of one hundred thousand times. At this time, an initial candidate set containing no less than two thousand low-energy conformations is output as the input data source for the subsequent coarse-grained screening stage.

[0026] Step S2, input the conformational instance into the coarse-grained scoring function module, the coarse-grained scoring function module adopts a fast energy estimation model based on the superposition of van der Waals potential and electrostatic potential, performs spatial adaptation degree evaluation in the binding pocket of the receptor for each conformational instance, eliminates invalid conformations obviously deviating from the geometric constraints of the target protein binding pocket, and retains a candidate conformation set with a score value in the top five percent, and the upper limit of the size of the candidate conformation set is set to five hundred conformational instances; In this step, the system first extracts the three-dimensional space boundary of the binding pocket from the crystal structure data of the target protein, constructs a bounding box through a convex hull algorithm, and calculates the principal axis direction and volume center coordinates. Then, for each conformation in the candidate conformation set, the Euclidean distance between the atomic center of mass and the volume center of the pocket is calculated. If the distance is greater than one and a half times the maximum side length of the pocket bounding box, it is directly determined as an invalid conformation and eliminated. For conformations that have not been eliminated, further calculate the matching degree of local geometric invariants with key residues in the pocket. Local geometric invariants include but are not limited to the spatial angle of hydrogen bond donor-acceptor pairs, the cosine similarity of the normal vector of the aromatic ring plane and the hydrophobic wall of the pocket, and the vector projection consistency of the charged group and the electrostatic potential gradient direction of the pocket. Each geometric invariant has an independent threshold, for example, the hydrogen bond angle must be between 120 degrees and 180 degrees, the cosine value of the aromatic ring normal vector must be greater than 0.85, and the projection consistency of the charged group must be greater than 0.9. If a conformation is below the corresponding threshold in any geometric invariant, it is marked as a low-priority conformation and is not eliminated but its processing priority in the subsequent refinement stage is reduced. After this round of screening, the size of the candidate conformation set is reduced to five to ten percent of the original number, significantly reducing the subsequent computational load while retaining a subset of conformations with potential binding capacity.

[0027] Step S3, perform local geometric refinement operation on the candidate conformation set, which includes three rounds of iterative optimization. In each round of iteration, a gradient-based spatial displacement correction is applied to the non-bonding atoms of the ligand molecule. The displacement step is dynamically adjusted by the convergence factor of the current round, and the initial value of the convergence factor is 0.2 angstrom, which decreases by 20% in each round. Step S4, after completing the local geometric refinement, start the residual structure deviation detection module, extract all pairs of atoms connected by rotatable bonds in the ligand molecule, calculate the change vector of the three-dimensional coordinates before and after refinement, and construct a local geometric invariant feature vector, which includes the change amount of bond length, bond angle, and dihedral angle. Step S5, input the local geometric invariant feature vector into the residual correction neural network for training, the residual correction neural network is a three-layer fully connected structure, the number of input layer nodes is three, the number of hidden layer nodes is twelve, the number of output layer nodes is three, the activation function adopts hyperbolic tangent function, the network weight is obtained by supervised training through five thousand groups of high-precision crystal structures and docking conformation pairs collected in advance, and the training target is to minimize the Euclidean distance between the output coordinate correction and the real crystal coordinates; Step S6, superimpose the coordinate correction output by the residual correction neural network on the atomic coordinates of the refined conformation to generate the final optimized conformation, and the final optimized conformation is used for subsequent binding free energy calculation and pharmacophore matching analysis; The core of this step is to build a deep neural network model with local geometric perception ability, the input of which is the atomic coordinate sequence of the conformation and its corresponding local geometric invariant feature vector, and the output is the corrected atomic coordinate offset. The network architecture adopts a three-layer convolution residual block stacking structure, each layer of convolution kernel size is three by three by three, step is one, padding mode is same padding, and activation function is a rectified linear unit. The first layer of convolution extracts the density distribution features of the local neighborhood of the atom, the second layer of convolution captures the small distortion mode of bond length and bond angle, and the third layer of convolution fuses multi-scale geometric constraint information and outputs the residual correction. In the training stage, the network takes the equilibrium conformation obtained by high-precision quantum mechanics calculation as the supervision signal, and uses the mean square error loss function for end-to-end optimization to ensure that it can accurately predict the atomic position deviation caused by force field approximation or sampling error. In the inference stage, for each input conformation, the network first normalizes its atomic coordinates to the local coordinate system with the pocket center as the origin, then extracts the geometric relationship features of the ten nearest neighbor residues in the pocket, including relative distance, azimuth angle and dihedral angle combination, and concatenates them into a 128-dimensional feature vector to input the network. The residual vector output by the network is superimposed on the original coordinates atom by atom to generate the corrected conformation.

[0028] Step S7, after the final optimized conformation is generated, a structure consistency verification process is performed, the root mean square deviation between the final optimized conformation and the original crystal conformation is calculated, if the root mean square deviation is greater than zero point five angstrom, a backtracking mechanism is triggered, a suboptimal conformation instance is selected from the candidate conformation set to re-execute the local geometric refinement and residual correction process, until the root mean square deviation meets the threshold requirement or the candidate conformation set is exhausted, the maximum number of retries of the backtracking mechanism is set to three, if the root mean square deviation threshold cannot be met after three retries, the ligand molecule is marked as a structure abnormal case, a warning log is output and the subsequent processing process of the molecule is terminated; After the correction, the system immediately calculates the weighted RMSD value of the conformation and the reference conformation. The weighted RMSD is based on the traditional atomic distance root mean square, and introduces a local geometric invariant weight factor to give higher weight to hydrogen bond key atoms, hydrophobic core atoms and charged anchor atoms, thereby suppressing the interference of coordinate jitter in non-functional regions on overall similarity evaluation. If the weighted RMSD value is lower than 0.5 angstrom, it is determined that the conformation passes the structural consistency verification and is retained for entering the final output queue; otherwise, it is marked as a to-be-backtracked conformation for abnormal case analysis.

[0029] The backtracking mechanism includes three progressive processing levels: the first level is local geometric resampling. For the conformation with a weighted RMSD value between 0.5 angstrom and 1.0 angstrom, the system locks the three atoms with the largest deviation, constructs a local sampling sphere with a radius of 3 angstroms centered on the three atoms, and performs high-density Monte Carlo perturbation in the sphere. After each perturbation, the weighted RMSD is recalculated. If a corrected conformation lower than 0.5 angstrom appears in twenty consecutive perturbations, the conformation is adopted and the backtracking is terminated. The second level is cross-conformation knowledge transfer. For the conformation with a weighted RMSD value higher than 1.0 angstrom but lower than 1.5 angstrom, the system retrieves the five conformations most similar to the local geometric features of the conformation from the verified conformation library, extracts the coordinate offset patterns of the corresponding atoms, generates a transfer correction vector by weighted averaging, and superimposes it on the to-be-corrected conformation for reevaluation. If the corrected RMSD meets the requirements, it is retained; otherwise, it enters the third level. The third level is global energy re-optimization. A high-precision molecular dynamics simulation engine is called to perform 500 picosecond explicit solvation simulation on the to-be-corrected conformation, and a trajectory snapshot is saved every 50 picoseconds. The conformation with the lowest energy and the smallest weighted RMSD is selected as the final correction result. If the RMSD is still higher than 1.5 angstrom after the three-level processing, the conformation is determined to be an invalid docking result and is permanently excluded. The backtracking mechanism ensures that the pipeline maintains high processing capacity while having sufficient fault tolerance and repair capability for edge cases, avoiding the misdeletion of high-quality conformations due to the limitations of a single algorithm.

[0030] Finally, all the conformations that pass the verification are subjected to final sorting and output. The sorting is based on a comprehensive scoring function composed of three weighted components: the first component is the total energy of the molecular force field of the corrected conformation, with a weight coefficient of 0.4; the second component is the inverse of the weighted RMSD value, with a weight coefficient of 0.3; and the third component is the normalized score of the local geometric invariant matching degree, with a weight coefficient of 0.3. After linearly weighting and summing the three scores, the final conformation list is generated in descending order. The top 50 conformations in the list are marked as high-confidence docking results and output to the user interface or downstream analysis module. At the same time, the system records the time consumption, correction amplitude and abnormal processing path of each conformation at each processing stage, generating a structured log file for algorithm performance evaluation and parameter optimization.

[0031] The specific operations of performing initial spatial pose traversal on the ligand molecule in step S1 include the following (the operations are performed by the molecular conformation sampling engine module): identifying the single bond connection relationship in the molecular topology, excluding the intra-ring bond and the terminal methyl rotation, and only performing torsion angle discretization processing on the single bond connecting two non-hydrogen atoms and not participating in the aromatic ring system; generating independent conformation instances within the preset torsion angle grid range according to the sampling density parameter of three to seven discrete angle values per degree of freedom; controlling the total number of conformation instances to be no more than the upper limit of the computing resources, and for ligand molecules containing more than ten rotatable bonds, starting the hierarchical sampling strategy, first performing full grid sampling on the main chain rotatable bonds, and then performing fixed one hundred random sampling on the side chain rotatable bonds.

[0032] The specific operations of performing spatial fitness evaluation on each conformation instance in step S2 include the following (the operations are performed by the coarse-grained scoring function module): calculating the van der Waals potential in the form of a combination of a twelve-power inverse repulsive term and a six-power inverse attractive term; calculating the electrostatic potential according to Coulomb's law, with the dielectric constant set to four and the atomic charge set to the pre-assigned molecular force field charge value; sorting the evaluation results, retaining the candidate conformation set with the top five percent of the score values, and additionally forcibly retaining the three conformation instances with the highest scores in the original conformation.

[0033] The specific operations of performing local geometric refinement on the candidate conformation set in step S3 include the following (the operations are performed by the local geometric refinement module): using a gradient descent algorithm with a momentum acceleration strategy, setting the momentum coefficient to zero point nine, setting the initial learning rate to zero point zero one, and multiplying the learning rate by zero point nine five after each iteration to decay; in each iteration, applying a spatial displacement correction to non-bonding atoms, with the displacement step being dynamically adjusted by the convergence factor of the current iteration; if the total energy change of the conformation of the last two iterations is less than zero point zero zero one electron volts, the optimization process of the current conformation is terminated in advance and marked as a converged state.

[0034] The specific operations of constructing the local geometric invariant feature vector in step S4 include the following (the operations are performed by the residual structure bias detection module): extracting all pairs of atoms connected by rotatable bonds in the ligand molecule; calculating the change vector of the three-dimensional coordinates before and after refinement; defining the bond length change as the absolute value of the difference between the refined bond length and the unrefined bond length; defining the bond angle change as the absolute value of the difference between the refined bond angle and the unrefined bond angle; defining the dihedral angle change as the absolute value of the difference between the refined dihedral angle and the unrefined dihedral angle; all angle quantities are expressed in radians, and are concatenated to form a three-dimensional local geometric invariant feature vector.

[0035] The residual correction neural network training operation in step S5 includes the following (this operation is performed by the residual correction neural network module): screening ligand-protein complex crystal structures with a resolution less than or equal to 0.8 angstrom from a protein database; using the same docking software and parameters to generate the corresponding calculated conformation; calculating the three-dimensional coordinate offset between the calculated conformation and the crystal conformation for each atom; extracting the geometric invariant features of the local chemical environment in which the atom is located; using the geometric invariant features as input and the coordinate offset as output to form a training sample pair; using the mean square error loss function, the adaptive moment estimation algorithm optimizer, setting the batch size to thirty-two, the training rounds to five hundred, saving a model snapshot every fifty rounds, and finally selecting the snapshot with the smallest validation set loss as the deployment model.

[0036] The specific operation of generating the final optimized conformation in step S6 includes the following (this operation is performed by the coordinate superposition module): limiting the magnitude of the correction output by the residual correction neural network, and the modulus of the single-atom coordinate correction should not exceed 0.3 angstrom; the excess is compressed to within the threshold range in proportion; the final optimized conformation is generated after superposition and is used for subsequent free energy calculation and pharmacophore matching analysis.

[0037] The specific operation of executing the structure consistency verification process in step S7 includes the following (this operation is performed by the structure consistency verification module): calculating the root mean square deviation between the final optimized conformation and the original crystal conformation, only for heavy atoms in the ligand molecule; performing a rigid superposition operation before calculation, and the superposition target is to minimize the root mean square deviation of the heavy atom coordinate set; if any bond length deviates from the standard value by more than 15% or any bond angle deviates from the standard value by more than 20 degrees, a local structure re-optimization subroutine is automatically triggered to optimize only the geometric parameters of the abnormal bond or angle under the condition of fixing the coordinates of the remaining atoms using the conjugate gradient method.

[0038] The operation when the backtracking mechanism is started in step S7 includes the following (this operation is performed by the backtracking control module): selecting the next unprocessed conformation instance in order from the candidate conformation set according to the original score value sorting order; re-executing the local geometry refinement and residual correction process; the maximum number of retries is set to three; after three retries fail, save all the original docking conformations and three optimized conformations to the abnormal case database, record the chemical identifier and failure reason code; at the same time, start the model fine-tuning process, when the number of abnormal cases accumulates to more than one hundred, use the new case data to perform ten rounds of incremental training on the existing network weights, set the learning rate to one tenth of the original training learning rate, and replace the original model file after training is completed.

[0039] In addition, during the process of steps S1 to S5, the intermediate calculation results of the processed conformation instances are stored by hash indexing through the data cache layer, the cache result is directly read when the same conformation appears again, the upper limit of the cache capacity is set to one hundred thousand records, and the least recently used strategy is used for elimination.

[0040] At the system architecture level, the system of the application is deployed on a multi-thread parallel computing framework, and the hardware basis is a computing node equipped with a sixteen-core central processor and an eight-gigabyte display memory graphics processor. Memory management adopts a shared address space model, and all modules share the same pre-allocated global memory pool to avoid frequent data copying and serialization overhead. The molecular conformation sampling engine module, the coarse-grained scoring function module, the residual correction neural network module and the backtracking control module are respectively bound to independent computing threads, and the seamless connection of data flow is realized through the shared memory pool. When a module completes the current batch processing, the result is immediately written to the lock-free ring buffer and the next module is notified to read, while the new task is obtained from the lock-free ring buffer to form a pipeline concurrent execution mode. The residual correction neural network module is specially optimized to a half-precision floating point operation mode, and the tensor core of the graphics processor is used to accelerate matrix multiplication, so that the single conformation correction time is stable within three milliseconds. A monitoring unit is also set in the system to collect the load rate, memory occupancy rate and filling state of the lock-free ring buffer of each thread in real time, dynamically adjust the batch size and thread priority, and ensure that the stable throughput and low delay response can be maintained in a high concurrency scenario.

[0041] In the data structure design, the conformation data is stored in a compact binary format, each conformation record contains an atom type identifier, a three-dimensional coordinate floating point array, a local geometric feature vector and a state flag bit, and the total length is fixed at forty thousand nine hundred and ninety-six bytes, which is convenient for memory alignment and batch reading and writing. The pocket geometry descriptor is pre-compiled as a lookup table, storing the coordinates, charge types and geometric constraint thresholds of key residues, and is loaded into the cache at one time to avoid repeated analysis of the protein data bank file. The neural network model parameters are quantized and compressed and solidified in the read-only storage area, and are directly loaded into the graphics processor display memory during inference to eliminate model loading delay. The log system adopts a circular overwrite writing strategy, retains the latest one hundred thousand records, each record contains a timestamp, a module identifier, a conformation hash value and key indicators, supports fast retrieval and statistical analysis according to conditions.

[0042] In terms of exception handling and fault tolerance mechanism, the system is built-in triple check protocol: the first is data integrity check, when data transmission between modules, additional cyclic redundancy check code, the receiving party verification failure request retransmission; the second is the rationality of the calculation result check, the output of the neural network coordinate offset is subjected to physical constraints, if the single atom offset is more than two angstrom or the bond length distortion is more than 20%, the abnormal interruption is triggered and the last stable state is rolled back; the third is the resource exhaustion protection, when the memory occupation rate continuously exceeds 90% or the thread blocking timeout exceeds 5 seconds, the system automatically releases the low priority task buffer, restarts the abnormal module and records the fault scene snapshot. The mechanism ensures that the system can maintain stable service when running continuously for a long time or processing extremely complex molecules, avoiding global crash caused by local failure.

[0043] The system of the application can be seamlessly integrated into the existing computer-aided drug design platform in actual deployment, as a standard component for docking post-processing and result verification, the input interface is compatible with mainstream molecular file formats, including but not limited to protein data bank, molecular connectivity table and simplified molecular linear input specification, the output interface supports structured query language database writing, hypertext transfer protocol application programming interface calling and flat file export. Users can adjust the threshold parameters of each stage, the backtracking processing depth and the output conformation quantity through the configuration file, adapt to the characteristics and screening requirements of different target proteins. After testing and verification on ten typical drug targets, compared with the traditional docking post-processing process, the system shortens the average processing time of single molecule from 12 seconds to 0.8 seconds, reduces the RMSD evaluation misjudgment rate by 37%, significantly improves the efficiency and reliability of virtual screening.

[0044] In the algorithm level, the local geometric invariant weighted RMSD calculation method introduced by the application can be summarized as the following formula: I. Weighted RMSD calculation formula: ; Wherein, N is the total number of atoms participating in the calculation (only heavy atoms), r i is the three-dimensional coordinates of the i-th atom in the conformation to be evaluated, r i ref is the three-dimensional coordinates of the corresponding atom in the reference conformation, w i is the geometric importance weight coefficient of the i-th atom, which is dynamically allocated according to the functional role of the atom in the hydrogen bond network, hydrophobic core or electrostatic anchoring, and the value ranges from 0.5 to 2.0.

[0045] II. Local geometric invariant matching degree calculation formula: ; Wherein, α, β, γ are the normalized weight coefficients of each term, the sum is one, and θHbond is the spatial angle of the hydrogen bond donor-acceptor pair (high match for 120°-180°), n aromatic is the normal vector of the ligand aromatic ring plane, n pocket : local normal vector of the pocket hydrophobic wall, E ligand is the local electrostatic potential direction vector of the ligand, E pocket is the pocket electrostatic potential direction vector.

[0046] III. Comprehensive scoring function formula: ; wherein, E current is the total energy of the molecular force field of the current conformation, E min is the lowest energy in the current conformation set, E max is the highest energy in the current conformation set, 1 / RMSD weighted is the weighted inverse RMSD, the smaller the RMSD, the higher the score, and MatchScore is the local geometric invariant matching score.

[0047] The above formula system constitutes the core algorithm basis of the application in the structure evaluation and conformation sorting stage, which fully integrates the physical and chemical prior knowledge and data-driven correction ability, and ensures that the evaluation results have both theoretical rationality and practical robustness.

[0048] The application integrates molecular conformation sampling, geometric constraint screening, neural network refinement, backtracking exception handling and parallel computing architecture to build a closed-loop rapid verification system, effectively solves the contradiction between docking accuracy and speed, significantly improves the reliability and efficiency of RMSD evaluation in high-throughput scenarios, and the modular design facilitates function expansion and parameter optimization, which can adapt to virtual screening tasks of different scales and provide strong technical support for new drug research and development.

[0049] The introduction of structure consistency verification and backtracking mechanism ensures the robustness of the results, and the automatic archiving of abnormal cases and the model incremental training function enable the system to have continuous evolution capability. The system can be seamlessly integrated into existing virtual screening platforms, significantly improving the accuracy and efficiency of lead compound identification, reducing false positive screening results caused by structural deviation, and shortening the drug discovery cycle.

[0050] The above description is only the preferred specific embodiment of the application; all the protection scope of the application is included, any person skilled in the art can make equivalent replacement or change according to the technical solution and improvement concept of the application within the technical range disclosed by the application, which should be covered in the protection scope of the application.

Claims

1. A rapid verification method for interface and RMSD optimization, characterized by: Includes the following operations: Step S1: Perform initial spatial orientation traversal on the ligand molecule through the molecular conformation sampling engine module: The traversal is based on a discretized twist angle grid divided by the rotation bond degrees of freedom, and each grid node corresponds to an independent conformation instance. The number of conformation instances is determined by a preset sampling density parameter. Step S2: Input the conformational instance into the coarse-grained scoring function module. The coarse-grained scoring function module adopts a fast energy estimation model based on the superposition of van der Waals potential and electrostatic potential to perform spatial fitness assessment within the receptor binding pocket for each conformational instance and retain the candidate conformation set with the score value in the top 5%. Step S3: Perform local geometric refinement operation on the candidate conformation set. The local geometric refinement operation includes three rounds of iterative optimization. In each round of iteration, a spatial displacement correction based on gradient descent is applied to the non-bonded atoms of the ligand molecule. The displacement step size is dynamically adjusted by the convergence factor of the current round. Step S4: After completing the local geometric refinement, start the residual structure deviation detection module, extract all rotatable bonded atom pairs in the ligand molecule, calculate the change vector of their three-dimensional coordinates before and after refinement, and construct the local geometric invariant eigenvector. Step S5: Input the local geometric invariant eigenvectors into the residual correction neural network for training; Step S6: Superimpose the coordinate correction amount output by the residual correction neural network onto the atomic coordinates of the refined conformation to generate the final optimized conformation. Step S7: After the final optimized conformation is generated, the structural consistency verification process is executed to calculate the root mean square deviation between the final optimized conformation and the original crystal conformation. If the root mean square deviation is greater than 0.5 angstroms, the backtracking mechanism is triggered to select the second-best-scoring conformation instance from the candidate conformation set and re-execute the local geometric refinement and residual correction process until the root mean square deviation meets the threshold requirement or the candidate conformation set is exhausted.

2. The rapid verification method for docking and RMSD optimization according to claim 1, characterized in that: The specific operations for performing the initial spatial orientation traversal of the ligand molecule in step S1 include the following: identifying single bond connections in the molecular topology, excluding intra-ring bonds and terminal methyl rotations, and performing torsion angle discretization only on single bonds connecting two non-hydrogen atoms that do not participate in the aromatic ring system; generating independent conformation instances within a preset torsion angle grid based on sampling density parameters of three to seven discrete angle values ​​per degree of freedom; controlling the total number of conformation instances to not exceed the upper limit of computational resources, and for ligand molecules containing more than ten rotatable bonds, initiating a hierarchical sampling strategy, first performing full-grid sampling on the main chain rotatable bonds, and then performing a fixed number of one hundred random samplings on the side chain rotatable bonds.

3. The rapid verification method for docking and RMSD optimization according to claim 2, characterized in that: The specific operations for performing spatial fitness evaluation on each conformation instance in step S2 include the following: calculating the van der Waals potential using a combination of a 12th power inverse repulsion term and a 6th power inverse attraction term; calculating the electrostatic potential using Coulomb's law, setting the dielectric constant to four, and using pre-allocated molecular force field charge values ​​for atomic charges; sorting the evaluation results, retaining the candidate conformation set with scores in the top 5%, and additionally forcibly retaining the three conformation instances with the highest scores in the original conformation.

4. The rapid verification method for docking and RMSD optimization according to claim 3, characterized in that: The specific operations for performing local geometric refinement on the candidate conformation set in step S3 include the following: using a gradient descent algorithm with momentum acceleration strategy, the momentum coefficient is set to 0.9, the initial learning rate is 0.01, and the learning rate is multiplied by 0.95 after each iteration to decay; in each iteration, spatial displacement correction is applied to non-bonded atoms, and the displacement step size is dynamically adjusted by the convergence factor of the current round. If the total energy change of the conformation in two consecutive iterations is less than 0.001 electron volts, the optimization process of the current conformation is terminated early and marked as a converged state.

5. The rapid verification method for docking and RMSD optimization according to claim 4, characterized in that: The specific operations for constructing the local geometric invariant eigenvector in step S4 include the following: extracting all rotatable bond-connected atom pairs in the ligand molecule; calculating the change vector of the three-dimensional coordinates before and after refinement; defining the bond length change as the absolute value of the difference between the bond length after refinement and the bond length before refinement; defining the bond angle change as the absolute value of the difference between the bond angle after refinement and the bond angle before refinement; defining the dihedral angle change as the absolute value of the difference between the dihedral angle after refinement and the dihedral angle before refinement; all angle quantities are expressed in radians and spliced ​​together to form the three-dimensional local geometric invariant eigenvector.

6. The rapid verification method for docking and RMSD optimization according to claim 5, characterized in that: Step S5, the residual correction neural network training operation, includes the following: screening ligand-protein complex crystal structures with a resolution of less than or equal to 0.8 Å from the protein database; generating corresponding computational conformations using the same docking software and parameters; calculating the three-dimensional coordinate offset between the computational conformation and the crystal conformation for each atom; extracting the geometric invariant features of the local chemical environment where the atom is located; using the geometric invariant features as input and the coordinate offset as output to form training sample pairs; using the mean squared error loss function, the adaptive moment estimation algorithm optimizer, setting the batch size to 32, setting the training rounds to 500, saving a model snapshot every 50 rounds, and finally selecting the snapshot with the minimum validation set loss as the deployment model.

7. The rapid verification method for docking and RMSD optimization according to claim 6, characterized in that: The specific operations for generating the final optimized conformation in step S6 include the following: limiting the magnitude of the correction amount output by the residual correction neural network, and ensuring that the magnitude of the correction amount for single-atom coordinates does not exceed 0.3 angstroms; compressing the excess portion proportionally to the threshold range; and generating the final optimized conformation after superposition for subsequent use in combination with free energy calculation and pharmacophore matching analysis.

8. The rapid verification method for docking and RMSD optimization according to claim 7, characterized in that: The specific operations of the structure consistency verification process in step S7 include the following: calculating the root mean square deviation between the final optimized conformation and the original crystal conformation, only for heavy atoms in the ligand molecule; performing a rigid superposition operation before calculation, with the superposition goal of minimizing the root mean square deviation of the heavy atom coordinate set; if any bond length is found to deviate from the standard value by more than 15 percent or the bond angle deviates from the standard value by more than 20 degrees, the local structure re-optimization subroutine is automatically triggered, using the conjugate gradient method to optimize only the geometric parameters of abnormal bonds or angles while fixing the coordinates of the remaining atoms.

9. The rapid verification method for docking and RMSD optimization according to claim 8, characterized in that: When the backtracking mechanism is activated in step S7, the following operations are performed: the next unprocessed conformation instance is selected sequentially from the candidate conformation set according to the original score value sorting order; the local geometry refinement and residual correction process is re-executed; the maximum number of retries is set to three; after three failed retries, the original docking conformation and the three optimized conformations are all saved to the abnormal case database, and the chemical identifier and failure reason code are recorded. Simultaneously, the model fine-tuning process is initiated. When the number of abnormal cases exceeds one hundred, the existing network weights are incrementally trained for ten rounds using new case data. The learning rate is set to one-tenth of the original training learning rate. After training is completed, the original model file is replaced.

10. The rapid verification method for docking and RMSD optimization according to claim 9, characterized in that: It also includes the following steps: During steps S1 to S5, the intermediate calculation results of the processed conformation instances are stored by hash index through the data caching layer. When the same conformation appears again, the cached result is read directly.

11. A rapid verification system with RMSD optimization, characterized by: Includes the following modules: The molecular conformation sampling engine module is used to perform initial spatial orientation traversal of ligand molecules; The coarse-grained scoring function module is used to perform spatial fit evaluation for each conformation instance; The local geometry refinement module is used to perform three rounds of iterative optimization on the candidate conformation set; The residual structure deviation detection module is used to construct local geometric invariant eigenvectors; The residual correction neural network module is used to receive the feature vector of local geometric invariants as input, and then output the three-dimensional coordinate correction through a three-layer fully connected structure. It supports dynamic incremental training. When the abnormal case database accumulates more than one hundred new cases, it automatically starts a ten-round incremental training process with the learning rate set to one-tenth of the original training learning rate. After training is completed, the original model file is replaced. The coordinate overlay module is used to overlay the coordinate correction values ​​output by the residual correction neural network onto the atomic coordinates of the refined conformation to generate the final optimized conformation. The structural consistency verification module is used to calculate the root mean square deviation between the final optimized conformation and the original crystal conformation; The backtracking control module is used to select the second-best scoring conformation instance from the candidate conformation set and re-execute the local geometric refinement and residual correction process.

12. The rapid verification system for docking and RMSD optimization according to claim 11, characterized in that: The molecular conformation sampling engine module, coarse-grained scoring function module, local geometry refinement module, residual structure deviation detection module, residual correction neural network module, coordinate superposition module, structural consistency verification module, and backtracking control module are all deployed within a unified computing framework. Multiple modules transfer data through a shared memory pool and support multi-threaded parallel processing.

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