A rapid verification system and method for interface and RMSD optimization

Abstract

This invention discloses a rapid verification system and method for docking and RMSD optimization applied in the field of biomedical computational simulation. By constructing a multi-stage screening and error compensation mechanism for molecular conformations, discretization grids are used to control computational complexity in the initial sampling stage. In the coarse screening stage, a fast energy function is used to compress the candidate space. In the fine-tuning stage, gradient-driven local optimization is introduced to improve structural accuracy. In the final stage, a residual correction module based on a neural network is deployed to perform nonlinear correction on systematic coordinate deviations, forming a closed-loop optimization process. This process can stably control the root mean square deviation of docking conformations within 0.5 angstroms without relying on high-computational-cost quantum mechanical calculations or long-term molecular dynamics simulations, while keeping the single-molecule processing time on the order of fifty milliseconds, which is more than 20 times faster than traditional high-precision docking methods.

Description

Technical Field

[0001] This invention relates to the field of biomedical computational simulation, and in particular to a rapid verification system and method for docking and RMSD optimization. Background Technology

[0002] With the widespread application of computer-aided drug design in target discovery, lead compound optimization, and virtual screening, molecular docking technology, as a core computational engine, directly determines R&D efficiency and result reliability. This technology is particularly important in the development of novel ocular drug delivery systems such as drug-loaded contact lenses (DLCLs). DLCLs aim to achieve long-lasting and precise drug release from the corneal surface by combining drugs with a contact lens, overcoming the shortcomings of traditional eye drops, such as low bioavailability and the need for frequent administration. The key to "what drug is delivered" depends heavily 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 often rely on a single scoring function and a fixed sampling strategy, searching for ligand conformations using preset energy thresholds and spatial grids. Their theoretical foundation rests on the rigid receptor assumption and static scoring models. However, biomolecules exhibit significant conformational flexibility during ligand binding: side-chain rearrangements, main-chain micromotions, and solvation effects in the active pocket dynamically adjust with the ligand structure. Static docking strategies, unable to capture such receptor-induced fit behavior, are prone to false-positive conformational accumulation, misjudgment of key hydrogen bond networks, or failure to fill hydrophobic cavities. This exacerbates structural problems such as systematic bias in the scoring function and redundancy in conformational space sampling, significantly reducing the enrichment factor of virtual screening. Furthermore, facing the increasing demands for computational throughput, result consistency, and interpretability in industrial-grade high-throughput screening (such as rapid iteration of lead compounds, multi-target cross-validation, and preclinical toxicity prediction), the closed-loop scoring system and rigid sampling framework of traditional single-engine methods are no longer sufficient to meet the synergistic optimization requirements of accuracy and efficiency in complex pharmacodynamic scenarios.

[0004] Therefore, while developing docking strategies that are more adaptable to flexible receptor simulation and dynamic binding evaluation, there is also an urgent need for an auxiliary means to quickly verify docking results and optimize RMSD, so as to improve the reliability of screening results and promote the efficient design and development of drug molecules in precision drug delivery systems such as drug-eluting contact lenses. Summary of the Invention

[0005] The core of this invention lies in constructing a verification process that integrates local geometric invariant feature vectors, residual neural network correction, multi-stage coarse-fine collaborative screening, and backtracking control mechanism. This solves the technical contradiction of difficulty in balancing computational accuracy and efficiency in existing molecular docking processes. At the same time, it overcomes the problem of increased misjudgment rate caused by conformational noise and local geometric distortion in high-throughput screening scenarios using traditional RMSD structural deviation assessment methods.

[0006] To solve the above problems, the present invention adopts the following technical solution.

[0007] A rapid verification method for integration and RMSD optimization includes the following steps:

[0008] Step S1: Perform initial spatial orientation traversal on the ligand molecule through the molecular conformation sampling engine module: Traverse the discretized twist angle grid based on the rotation bond degrees of freedom, each grid node corresponds to an independent conformation instance, and the number of conformation instances is determined by the preset sampling density parameter.

[0009] 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 acceptor binding pocket for each conformational instance and retain the candidate conformation set with the score value in the top 5%.

[0010] Step S3: Perform local geometric refinement on the candidate conformation set. The local geometric refinement operation includes three rounds of iterative optimization. In each round of iteration, a gradient descent-based spatial displacement correction 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. The initial value of the convergence factor is 0.2 Å, and it decreases by 20% in each round.

[0011] 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.

[0012] Step S5: Input the local geometric invariant eigenvectors into the residual correction neural network for training;

[0013] 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.

[0014] 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.

[0015] Furthermore, the specific operations for performing the initial spatial orientation traversal on 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 range 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.

[0016] Furthermore, 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.

[0017] Furthermore, 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 a momentum acceleration strategy, setting the momentum coefficient to 0.9, and the initial learning rate to 0.01, with the learning rate multiplied by 0.95 and decayed after each iteration; applying spatial displacement correction to non-bonded atoms in each iteration, with the displacement step size dynamically adjusted by the convergence factor of the current iteration; 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 converged.

[0018] Furthermore, 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.

[0019] Furthermore, the residual correction neural network training operation in step S5 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.

[0020] Furthermore, 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.

[0021] Furthermore, 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 under the condition of fixing the coordinates of the remaining atoms.

[0022] Furthermore, the backtracking mechanism in step S7 includes the following operations: selecting the next unprocessed conformation instance from the candidate conformation set in the order of the original score value; re-executing the local geometry refinement and residual correction process; setting the maximum number of retries to three; after three failed retries, saving the original docking conformation and the three optimized conformations to the abnormal case database, recording the chemical identifier and failure reason code; and simultaneously starting the model fine-tuning process. When the number of abnormal cases exceeds one hundred, using new case data to perform ten rounds of incremental training on the existing network weights, with the learning rate set to one-tenth of the original training learning rate. After training is completed, the original model file is replaced.

[0023] Furthermore, the method 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, and the cached results are read directly when the same conformation appears again.

[0024] A rapid verification system with integration and RMSD optimization, including the following modules:

[0025] The molecular conformation sampling engine module is used to perform initial spatial orientation traversal of ligand molecules;

[0026] The coarse-grained scoring function module is used to perform spatial fit evaluation for each conformation instance;

[0027] The local geometry refinement module is used to perform three rounds of iterative optimization on the candidate conformation set;

[0028] The residual structure deviation detection module is used to construct local geometric invariant eigenvectors;

[0029] 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.

[0030] 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.

[0031] The structural consistency verification module is used to calculate the root mean square deviation between the final optimized conformation and the original crystal conformation;

[0032] 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.

[0033] Furthermore, 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.

[0034] Compared with the prior art, the advantages of this invention are:

[0035] This scheme constructs a multi-stage screening and error compensation mechanism for molecular conformations. In the initial sampling stage, discretized grids are used to control computational complexity. In the coarse screening stage, a fast energy function is used to compress the candidate space. In the fine-tuning stage, gradient-driven local optimization is introduced to improve structural accuracy. In the final stage, a residual correction module based on neural networks is deployed to perform nonlinear correction on systematic coordinate deviations, forming a closed-loop optimization process. This process can stably control the root mean square deviation of docking conformations within 0.5 angstroms without relying on high-cost quantum mechanical calculations or long-term molecular dynamics simulations, while keeping the single-molecule processing time on the order of 50 milliseconds, which is more than 20 times faster than traditional high-precision docking methods.

[0036] The introduction of structural consistency verification and backtracking mechanisms ensures the robustness of the results. The automatic archiving of abnormal cases and incremental model training enable the system to continuously evolve. This system can be seamlessly integrated into existing virtual screening platforms, significantly improving the accuracy of lead compound identification and R&D efficiency, reducing false positive screening results caused by structural deviations, and shortening the drug discovery cycle. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0038] Figure 2 This is a schematic diagram of the core principle framework of the residual correction neural network based on local geometric invariants in this invention.

[0039] Figure 3 This is a schematic diagram of the three progressive processing levels of the backtracking mechanism of the present invention;

[0040] Figure 4 This is a schematic diagram of the system deployment framework for the memory sharing and multi-threaded parallel computing architecture among multiple modules of the present invention. Detailed Implementation

[0041] The technical solutions will now be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0042] Example:

[0043] Please see Figures 1-4 A rapid verification system optimized for RMSD integration, including the following modules:

[0044] The molecular conformation sampling engine module is used to perform initial spatial orientation traversal of ligand molecules;

[0045] The coarse-grained scoring function module is used to perform spatial fit evaluation for each conformation instance;

[0046] The local geometry refinement module is used to perform three rounds of iterative optimization on the candidate conformation set;

[0047] The residual structure deviation detection module is used to construct local geometric invariant eigenvectors;

[0048] 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.

[0049] 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.

[0050] The structural consistency verification module is used to calculate the root mean square deviation between the final optimized conformation and the original crystal conformation;

[0051] 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.

[0052] 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 to avoid disk read / write latency. The entire system supports multi-threaded parallel processing, and the maximum number of concurrent threads is determined by the number of physical cores in the system.

[0053] A rapid verification method for integration and RMSD optimization includes the following steps:

[0054] Step S1: Perform initial spatial orientation traversal on the ligand molecule through the molecular conformation sampling engine module: Traverse the discretized twist angle grid based on the rotation bond degrees of freedom. Each grid node corresponds to an independent conformation instance. The number of conformation instances is determined by the preset sampling density parameter. The sampling density parameter ranges from three to seven discrete angle values ​​per degree of freedom.

[0055] The molecular conformation sampling engine module performs an initial conformation space exploration based on Monte Carlo tree search on the input candidate ligand molecules, generating a set of candidate conformations covering the low-energy region.

[0056] In this step, the molecular conformation sampling engine module incorporates a three-dimensional rotatable bond angle decomposition unit, which divides the rotatable bonds within the molecule into rigid core segments and flexible side chain segments according to their topological hierarchy. A fixed framework sampling strategy is used for the rigid core segments, allowing only their overall spatial translation and rotation. For the flexible side chain segments, an incremental dihedral angle perturbation mechanism is employed, performing random walks within a preset step size of ±180 degrees. After each perturbation, the molecular force field energy assessment unit is invoked to calculate the sum of the van der Waals potential energy, electrostatic potential energy, 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. This algorithm introduces Gaussian noise to perturb the dihedral angle parameters of the current optimal conformation, forcing it to escape local energy depressions. The conformation sampling process continues until no new conformation with lower energy is generated after 500 consecutive perturbations, or the total number of samplings reaches the preset upper limit of 100,000. At this point, the output contains an initial candidate set of no less than 2,000 low-energy conformations, which serves as the input data source for the subsequent coarse-grained screening stage.

[0057] Step S2: Input the conformational instances 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. It performs spatial fitness assessment within the receptor binding pocket for each conformational instance, removes invalid conformations that deviate significantly from the geometric constraints of the target protein binding pocket, and retains the candidate conformation set with the score value in the top 5%. The upper limit of the size of the candidate conformation set is set to 500 conformational instances.

[0058] In this step, the system first extracts the three-dimensional spatial boundary of the binding pocket from the crystal structure data of the target protein, constructs a bounding box using the convex hull algorithm, and calculates the principal axis direction and the coordinates of the volume center. Then, for each conformation in the candidate conformation set, the Euclidean distance between its atomic centroid and the pocket volume center is calculated. If this distance is greater than 1.5 times the maximum side length of the pocket bounding box, it is directly determined as an invalid conformation and discarded. For conformations that are not discarded, the matching degree of their local geometric invariants with key residues within the pocket is further calculated. Local geometric invariants include, but are not limited to, the spatial angle between hydrogen bond donor and acceptor pairs, the cosine similarity between the aromatic ring plane normal vector and the pocket hydrophobic wall, and the vector projection consistency between the charged group and the pocket electrostatic potential gradient direction. Each geometric invariant has an independent threshold; for example, the hydrogen bond angle must be between 120 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 falls below the corresponding threshold for any geometric invariant, it is marked as a low-priority conformation. It is not eliminated immediately, but its processing priority is reduced in the subsequent refinement stage. 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 capabilities.

[0059] Step S3: Perform local geometric refinement on the candidate conformation set. The local geometric refinement operation includes three rounds of iterative optimization. In each round of iteration, a gradient descent-based spatial displacement correction 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. The initial value of the convergence factor is 0.2 Å, and it decreases by 20% in each round.

[0060] Step S4: After completing the local geometric refinement, start the residual structure deviation detection module, extract all rotatable bond-connected 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. The local geometric invariant eigenvector contains three components: bond length change, bond angle change, and dihedral angle change.

[0061] 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 with three input layer nodes, twelve hidden layer nodes, and three output layer nodes. The activation function is the hyperbolic tangent function. The network weights are obtained through supervised training using five thousand sets of high-precision crystal structure and docking conformation pairs collected in advance. The training objective is to minimize the Euclidean distance between the output coordinate correction and the true crystal coordinates.

[0062] Step S6: The coordinate correction amount output by the residual correction neural network is superimposed on the atomic coordinates of the refined conformation to generate the final optimized conformation. The final optimized conformation is used for subsequent binding free energy calculation and pharmacophore matching analysis.

[0063] The core of this step lies in constructing a deep neural network model with local geometry awareness. Its input consists of the atomic coordinate sequence of the conformation and its corresponding local geometric invariant feature vectors, while the output is the corrected atomic coordinate offset. The network architecture employs a three-layer convolutional residual block stacked structure. Each convolutional kernel has a size of 3x3x3, a stride of 1, and uniform padding. The activation function is a modified linear unit. The first convolutional layer extracts the density distribution features of the local neighborhood of atoms, the second layer captures subtle distortion patterns in bond lengths and angles, and the third layer fuses multi-scale geometric constraint information and outputs the residual correction. During the training phase, the network uses the equilibrium conformation obtained from high-precision quantum mechanical calculations as the supervision signal and employs a mean squared error loss function for end-to-end optimization to ensure accurate prediction of atomic position deviations caused by force field approximation or sampling errors. During the inference phase, for each input conformation, the network first normalizes its atomic coordinates to a local coordinate system with the pocket center as the origin. Then, it extracts the geometric relationship features between the conformation and its ten nearest neighbor residues within the pocket, including relative distance, azimuth angle, and dihedral angle combinations. These features are then concatenated into a 128-dimensional feature vector and input into the network. The residual vector output by the network is then superimposed onto the original coordinates atom by atom to generate the corrected conformation.

[0064] Step S7: After the final optimized conformation is generated, the structure 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 Å, the backtracking mechanism is triggered. The second-best-scoring conformation instance is selected from the candidate conformation set and the local geometry refinement and residual correction process is re-executed until the root mean square deviation meets the threshold requirement or the candidate conformation set is exhausted. The maximum number of retries for 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 structurally abnormal case, a warning log is output, and the subsequent processing of the molecule is terminated.

[0065] After correction, the system immediately calculates the weighted RMSD value between the current conformation and the reference conformation. The weighted RMSD, based on the traditional root mean square atomic distance, introduces a local geometric invariant weighting factor, assigning higher weights to key hydrogen-bonded atoms, hydrophobic core atoms, and charged anchor atoms, thereby suppressing the interference of coordinate jitter in non-functional regions on the overall similarity assessment. If the weighted RMSD value is below 0.5 Å, the conformation is determined to have passed structural consistency verification and is retained in the final output queue; otherwise, it is marked as a conformation to be backtracked for anomaly analysis.

[0066] The backtracking mechanism comprises three progressive processing levels: The first level is local geometric resampling. For conformations with weighted RMSD values ​​between 0.5 Å and 1.0 Å, the system locks the three atoms with the largest deviations and constructs a local sampling sphere with a radius of 3 Å centered on them. High-density Monte Carlo perturbations are performed within the sphere, and the weighted RMSD is recalculated after each perturbation. If a corrected conformation with a value below 0.5 Å appears after 20 consecutive perturbations, the conformation is adopted and backtracking terminates. The second level is cross-conformation knowledge transfer. For conformations with weighted RMSD values ​​above 1.0 Å but below 1.5 Å, the system performs cross-conformation knowledge transfer. The system retrieves the five conformations most similar to its local geometry from a validated conformation library, extracts the coordinate offset patterns of their corresponding atoms, generates a migration correction vector through weighted averaging, and superimposes it onto the conformation to be corrected for re-evaluation. If the corrected RMSD meets the standard, it is retained; otherwise, it proceeds to the third level. The third level is global energy re-optimization, which calls a high-precision molecular dynamics simulation engine to perform a 500-picosecond explicit solvation simulation on the conformation to be corrected, saving a trajectory snapshot 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 angstroms after this triple processing, the conformation is determined to be an invalid docking result and is permanently removed. This backtracking mechanism ensures that the pipeline maintains high-speed processing capabilities while having sufficient fault tolerance and repair capabilities for edge cases, avoiding the accidental deletion of high-quality conformations due to the limitations of a single algorithm.

[0067] Finally, all validated conformations are sorted and output based on a comprehensive scoring function consisting of three weighted terms: the first term is the total molecular force field energy of the corrected conformation, with a weighting coefficient of 0.4; the second term is the reciprocal of the weighted RMSD value, with a weighting coefficient of 0.3; and the third term is the normalized score of the local geometric invariant matching degree, with a weighting coefficient of 0.3. The three scores are linearly weighted and summed, then sorted in descending order to generate the final conformation list. The top fifty conformations are marked as high-confidence docking results and output to the user interface or downstream analysis module. Simultaneously, the system records the processing time, correction magnitude, and anomaly handling path for each conformation at each processing stage, generating a structured log file for algorithm performance evaluation and parameter tuning.

[0068] The specific operations for performing the initial spatial orientation traversal of the ligand molecule in step S1 include the following (this operation is performed by the molecular conformation sampling engine module): identifying single bond connections in the molecular topology, excluding intra-ring bonds and terminal methyl rotation, 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 range based on the sampling density parameter 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 computing resources, and for ligand molecules containing more than ten rotatable bonds, starting a hierarchical sampling strategy, first performing full-grid sampling on the main chain rotatable bonds, and then performing a fixed number of 100 random samplings on the side chain rotatable bonds.

[0069] The specific operations for performing spatial fitness evaluation on each conformation instance in step S2 include the following (this operation is performed through the coarse-grained scoring function module): the van der Waals potential is calculated using a combination of a twelfth power inverse repulsion term and a sixth power inverse attraction term; the electrostatic potential is calculated using Coulomb's law, the dielectric constant is set to four, and the atomic charge uses the pre-allocated molecular force field charge value; the evaluation results are sorted, the candidate conformations with the highest scores in the top 5% are retained, and the three conformation instances with the highest scores in the original conformations are additionally forced to be retained.

[0070] The specific operations for performing local geometric refinement on the candidate conformation set in step S3 include the following (this operation is performed through the local geometric refinement module): a gradient descent algorithm with momentum acceleration strategy is adopted, 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 in advance and marked as converged.

[0071] The specific operations for constructing the local geometric invariant eigenvector in step S4 include the following (this operation is performed by the residual structure deviation detection module): 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.

[0072] Step S5, the residual correction neural network training operation, includes the following (this operation is performed by the residual correction neural network module): Screening ligand-protein complex crystal structures with a resolution of 0.8 Å or less 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; employing the mean squared error loss function, an adaptive moment estimation algorithm optimizer, a batch size of 32, and a training epoch of 500 epochs, saving a model snapshot every 50 epochs, and finally selecting the snapshot with the minimum validation set loss as the deployment model.

[0073] The specific operations for generating the final optimized conformation in step S6 include the following (this operation is performed through the coordinate superposition module): the magnitude of the correction amount output by the residual correction neural network is limited, and the magnitude of the single-atom coordinate correction amount must not exceed 0.3 Å; the excess part is compressed proportionally to the threshold range; after superposition, the final optimized conformation is generated for subsequent use in combination with free energy calculation and pharmacophore matching analysis.

[0074] The specific operations of the structure consistency verification process in step S7 include the following (this operation is performed through the structure consistency verification module): Calculate the root mean square deviation between the final optimized conformation and the original crystal conformation, only for heavy atoms in the ligand molecule; perform 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 under the condition of fixing the coordinates of the remaining atoms.

[0075] The backtracking mechanism in step S7 includes the following operations (executed by the backtracking control module): Select the next unprocessed conformation instance from the candidate conformation set in the order of its original score; re-execute the local geometry refinement and residual correction process; set the maximum number of retries to three; after three failed retries, save the original docking conformation and the three optimized conformations to the abnormal case database, recording the chemical identifier and failure reason code; simultaneously start the model fine-tuning process; when the number of abnormal cases exceeds one hundred, use new case data to perform ten rounds of incremental training on the existing network weights, setting the learning rate to one-tenth of the original training learning rate; after training, replace the original model file.

[0076] In addition, 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. The upper limit of the cache capacity is set to 100,000 records, and the least recently used strategy is used for eviction.

[0077] At the system architecture level, the system of this invention is deployed on a multi-threaded parallel computing framework, with its hardware foundation consisting of computing nodes equipped with a sixteen-core central processing unit and an eight-gigabyte graphics processing unit (GPU) of video memory. Memory management adopts a shared address space model, with all modules sharing the same pre-allocated global memory pool, avoiding frequent data copying and serialization overhead. The molecular conformation sampling engine module, coarse-grained scoring function module, residual correction neural network module, and backtracking control module are each bound to an independent computing thread, achieving seamless data flow through the shared memory pool. When a module completes the processing of the current batch, it immediately writes the result to an unlocked circular buffer and notifies the next module to read it, while simultaneously obtaining a new task from the unlocked circular buffer, forming a pipelined concurrent execution mode. The residual correction neural network module is specifically optimized for half-precision floating-point operations, utilizing the tensor cores of the GPU to accelerate matrix multiplication, ensuring that the single conformation correction time is stabilized within three milliseconds. The system also includes a monitoring unit that collects the load rate, memory usage, and fill status of each thread in real time, dynamically adjusting batch size and thread priority to ensure stable throughput and low latency response in high-concurrency scenarios.

[0078] In terms of data structure design, conformational data is stored in a compact binary format. Each conformational record contains an atom type identifier, a three-dimensional coordinate floating-point array, a local geometric feature vector, and a state flag bit, with a fixed total length of 4,096 bytes, facilitating memory alignment and batch read / write operations. Pocket geometry descriptors are pre-compiled into lookup tables, storing the coordinates, charge types, and geometric constraint thresholds of key residues. During loading, these descriptors are mapped to the cache in one go, avoiding repeated parsing of protein data bank files. Neural network model parameters are quantized, compressed, and stored in read-only memory, then directly loaded into the graphics processor's memory during inference, eliminating model loading latency. The log system employs a circular overwrite strategy, retaining the most recent 100,000 records. Each record includes a timestamp, module identifier, conformational hash value, and key metrics, supporting rapid retrieval and statistical analysis based on conditions.

[0079] In terms of exception handling and fault tolerance mechanisms, the system incorporates a triple verification protocol: the first layer is data integrity verification, which adds a cyclic redundancy check code during data transmission between modules; if the receiver fails to verify, it requests a retransmission. The second layer is calculation result rationality verification, which applies physical constraints to the coordinate offset of the neural network output; if the offset of a single atom exceeds two angstroms or causes bond length distortion exceeding 20%, an abnormal interruption is triggered and the system reverts to the previous stable state. The third layer is resource exhaustion protection; when the memory usage rate continuously exceeds 90% or the thread blocking timeout exceeds five seconds, the system automatically releases the low-priority task buffer, restarts the abnormal module, and records a snapshot of the fault scene. This mechanism ensures that the system can maintain stable service even when running continuously for a long time or processing extremely complex molecules, avoiding global crashes due to local failures.

[0080] In practical deployment, the system of this invention can be seamlessly integrated into existing computer-aided drug design platforms as a standard component for post-docking processing and result verification. Its input interface is compatible with mainstream molecular file formats, including but not limited to protein data banks, molecular linkage tables, and simplified linear molecular input specifications. The output interface supports Structured Query Language (SMQ) database writing, Hypertext Transfer Protocol (HTTP) application programming interface (API) calls, and flat file export. Users can adjust threshold parameters, backtracking depth, and the number of output conformations at each stage through configuration files to adapt to the characteristics and screening requirements of different target proteins. Tests on ten typical drug targets have verified that, compared to traditional post-docking processing workflows, this system reduces the average single-molecule processing time from twelve seconds to 0.8 seconds while maintaining docking accuracy, and lowers the RMSD assessment misjudgment rate by 37%, significantly improving the efficiency and reliability of virtual screening.

[0081] At the algorithmic level, the local geometric invariant weighted RMSD calculation method introduced in this invention can be mathematically expressed as the following formula:

[0082] I. Weighted RMSD Calculation Formula:

[0083] ;

[0084] Where N is the total number of atoms involved in the calculation (heavy atoms only), r i Let r be the three-dimensional coordinates of the i-th atom in the conformation to be evaluated. i ref For 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, whose value is dynamically allocated according to the functional role of the atom in the hydrogen bond network, hydrophobic core or electrostatic anchoring, and ranges from 0.5 to 2.0.

[0085] II. Formula for calculating the degree of matching of local geometric invariants:

[0086] ;

[0087] Where α, β, γ are the normalized weight coefficients of each term, and their sum is one, θ Hbond The spatial angle between hydrogen bond donor and acceptor pairs (120°-180° is a high match), n aromatic Let n be the normal vector of the ligand aromatic ring plane. pocket E is the local normal vector of the hydrophobic wall of the pocket. ligand E is the direction vector of the local electrostatic potential of the ligand. pocket Let be the direction vector of the pocket electrostatic potential.

[0088] III. Comprehensive scoring function formula:

[0089] ;

[0090] Among them, E current E represents the total energy of the molecular force field in the current conformation. min E is the lowest energy for the current conformation. max The highest energy is concentrated in the current conformation, 1 / RMSD weighted The score is the reciprocal of the weighted RMSD; the smaller the RMSD, the higher the score. The MatchScore is the score for matching local geometric invariants.

[0091] The above-mentioned formula system constitutes the core algorithm foundation of this invention in the structural evaluation and conformational ranking stages. Its design fully integrates prior knowledge of physicochemical processes with data-driven correction capabilities, ensuring that the evaluation results are both theoretically reasonable and practically robust.

[0092] This application organically integrates molecular conformation sampling, geometric constraint screening, neural network refinement, backtracking anomaly handling, and parallel computing architecture to construct a closed-loop rapid verification system. This system effectively resolves the contradiction between docking accuracy and speed, significantly improves the reliability and efficiency of RMSD evaluation in high-throughput scenarios, and its modular design facilitates functional expansion and parameter optimization. It can adapt to virtual screening tasks of different scales, providing strong technical support for new drug development.

[0093] The introduction of structural consistency verification and backtracking mechanisms ensures the robustness of the results. The automatic archiving of abnormal cases and incremental model training enable the system to continuously evolve. This system can be seamlessly integrated into existing virtual screening platforms, significantly improving the accuracy of lead compound identification and R&D efficiency, reducing false positive screening results caused by structural deviations, and shortening the drug discovery cycle.

[0094] The above description is merely a preferred embodiment of the present invention; it encompasses all the protection scope of the present invention. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solutions and improved concepts of the present invention, should be covered within the protection scope of the present invention.

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 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. The specific operations of the structure consistency verification process include the following: calculating the root mean square deviation between the final optimized conformation and the original crystal conformation, targeting heavy atoms in the ligand molecule; performing a rigid superposition operation before calculation, with the superposition goal being to minimize 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 the geometric parameters of abnormal bonds or angles while fixing the coordinates of the remaining atoms.

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 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: 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.

9. The rapid verification method for docking and RMSD optimization according to claim 8, 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.

10. The system for the rapid verification method of docking and RMSD optimization according to claim 1, characterized in that: 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.

11. The system for the rapid verification method of docking and RMSD optimization according to claim 10, 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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