A computer-aided virtual screening and design method for coal flotation reagents

Abstract

The present invention discloses a computer-aided coal flotation agent virtual screening and design method, comprising the following steps: (1) selecting a suitable pharmacophore model according to the performance index of the pharmacophore; (2) obtaining the lowest energy conformation of a coal 3D molecule; (3) constructing an agent-coal interaction system, performing molecular dynamics simulation on the system, and recording the position of the interaction between the two; (4) constructing a cubic active pocket for virtual screening of agents; (5) obtaining agent molecules that meet the query structure; (6) screening molecules with larger energy; (7) determining template molecules; (8) constructing a three-dimensional quantitative structure-activity relationship model; (9) evaluating robustness and predictability and selecting the best 3D-QSAR model; and (10) selecting agent molecules. The method can shorten the flotation agent research and development cycle, reduce the flotation agent development cost, and provide theoretical guidance for agent structure optimization.

Description

Technical Field

[0001] The invention relates to the technical field of coal flotation reagents, and in particular to a computer-aided coal flotation reagent virtual screening and design method. Background Art

[0002] The efficient and clean utilization of coal is of great significance to the processing and transformation of improving resource utilization efficiency and reducing pollutant emissions. Coal preparation, as the source technology for clean utilization of coal, plays a pioneering role in the clean utilization of coal.

[0003] With the deepening of coal processing and utilization, the resource endowment presents the characteristics of poor, fine and mixed. Flotation can achieve economical and efficient separation of fine-grained minerals according to the difference in mineral surface wettability. By adding flotation agents such as collectors during the flotation process, the directional regulation of the hydrophilicity and hydrophobicity of the mineral surface can be achieved, thereby achieving the purpose of efficient flotation. The efficient development and utilization of coal is seriously restricted by factors such as the large number of reagents, the low efficiency of conventional unit flotation experiments, the expensive flotation reagents, and the large differences in coal quality in different coal preparation plants. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a computer-aided coal flotation reagent virtual screening and design method.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] The present invention provides a computer-aided coal flotation reagent virtual screening and design method, comprising the following steps:

[0007] (1) Using Sybyl-X software, a series of coal flotation agent molecules with the same parent structure are flexibly superimposed in the internal space to generate multiple conformations, and the conformations generated by each compound are rigidly superimposed to generate multiple pharmacophore models, and the appropriate pharmacophore model is selected according to the performance indicators of the pharmacophore model;

[0008] (2) Obtain 1.3kg / cm through a small floating test 3 The low-ash coal was obtained, and the structural information of the obtained low-ash coal was detected. The coal molecular plane structure was constructed based on the detected information. The coal molecular plane structure was corrected using ACD / CNMR predictor software, and the coal molecular structure was optimized using Material Studio software to obtain the coal 3D molecular minimum energy conformation.

[0009] (3) Drawing a series of coal flotation agent molecules with the same parent structure using Material Studio software, constructing an agent-coal interaction system together with the coal molecules optimized in step (2), performing molecular dynamics simulation on the system, and recording the positions of the interaction between the two;

[0010] (4) Using Autodock software, a cubic active pocket is constructed with the action position in step (3) as the center for virtual screening of drugs;

[0011] (5) using Sybyl-X software, converting the pharmacophore model selected in step (1) into a three-dimensional query structure, and performing a preliminary screening of an organic compound database based on the three-dimensional query structure to obtain drug molecules that meet the query structure;

[0012] (6) using Autodock software to perform molecular docking on the drug molecules screened out in step (5) in the active pocket region obtained in step (4), completing the secondary screening of the organic compound database and obtaining the molecular docking energy of the drug-coal active site; sorting the drug molecules by the molecular docking energy, and screening 10 to 15 drug molecules with larger energy;

[0013] (7) performing a laboratory unit flotation test on the reagent molecules selected in step (6); then performing data processing on the flotation results, calculating the combustible recovery rate corresponding to each flotation reagent, and determining the reagent with the highest combustible recovery rate as the template molecule;

[0014] (8) using the combustible recovery rate obtained in step (7) as the performance index of each agent molecule, and constructing a three-dimensional quantitative structure-activity relationship model using the least squares method;

[0015] (9) The three-dimensional quantitative structure-activity relationship model constructed in step (8) was cross-validated by the leave-one-out method to obtain the cross-validation coefficient q 2 The consistency of the three-dimensional quantitative structure-activity relationship model constructed in step (8) was tested by non-cross-validation method to obtain the standard deviation SEE and Fischer test value F of the three-dimensional quantitative structure-activity relationship model. 2 , SEE value and F value, evaluate the robustness and predictiveness and select the best 3D-QSAR model;

[0016] (10) Using Sybyl-X software, draw the three-dimensional isopotential map of the 3D-QSAR model, analyze the structural optimization information of the template molecule proposed by the three-dimensional isopotential map, and select the drug molecule based on the analysis results.

[0017] Preferably, the performance indicators of the pharmacophore model in step (1) are lower Energy value, higher Specificity, Sterics, H-bond and Mol-Qry values.

[0018] Preferably, in step (4), the side length of the active pocket of the cube is 30 angstroms.

[0019] Preferably, the 3D-QSAR model in step (9) includes a comparative molecular field analysis method model and a comparative molecular similarity index analysis method model.

[0020] Preferably, the comparative molecular field analysis method model is the sum of one or both of steric field and electrostatic field, and the comparative molecular similarity index analysis method model is the sum of one or more of steric field, electrostatic field, hydrophobic field, hydrogen bond donor field and hydrogen bond acceptor field.

[0021] Preferably, when selecting the best 3D-QSAR model in step (9), q 2 The best value is the one with the largest value and 0.5 as the qualified boundary, the best value is the one with the largest value and 100 as the qualified boundary, and the best value is the one with the smallest SEE value.

[0022] The beneficial effects of the present invention are:

[0023] 1. The present invention can shorten the research and development cycle of flotation agents and reduce the development cost of flotation agents through a virtual screening strategy that combines a pharmacophore model with molecular docking technology.

[0024] 2. The present invention can provide an optimization direction for the molecular structure of the template drug in the form of a three-dimensional potential energy diagram, and can provide theoretical guidance for drug structure optimization.

[0025] 3. The present invention uses Sybyl-X, AutoDock, Material Studio and other software, and combines molecular docking, molecular dynamics, and quantitative structure-activity relationship methods to form a complete flotation agent screening and optimization system, which not only has profound theoretical significance, but also has practical application value. The screening and design of high-efficiency agents for specific types of coal will greatly improve the economic benefits of coal preparation plants, improve resource utilization, and have good social benefits. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in combination with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] This embodiment provides a computer-aided coal flotation reagent virtual screening and design method, comprising the following steps:

[0028] (1) Using Sybyl-X software, a series of coal flotation reagent molecules with the same parent structure are flexibly superimposed in the internal space to generate multiple conformations, and the conformations generated by various alkanes, carboxylic acids, esters and other compounds are rigidly superimposed to generate multiple pharmacophore models. The appropriate pharmacophore model is selected according to the performance indicators of the pharmacophore model, where the performance indicators of the pharmacophore model are lower Energy value, higher Specificity, Sterics, H-bond and Mol-Qry values;

[0029] (2) Obtain 1.3kg / cm through a small floating test 3 The low-ash coal was obtained, and the structural information of the obtained low-ash coal, such as the aromatic skeleton and side chain, was detected. The 2D planar structure of the coal molecule was constructed based on the detected information. The ACD / CNMR predictor software was used to correct the planar structure of the coal molecule. The Material Studio software was used to optimize the coal molecular structure to obtain the lowest energy conformation of the coal 3D molecule.

[0030] (3) Drawing a series of coal flotation agent molecules with the same parent structure using Material Studio software, constructing an agent-coal interaction system together with the coal molecules optimized in step (2), performing molecular dynamics simulation on the system, and recording the action positions of the agent molecules on the coal molecular structure;

[0031] (4) Using Autodock software, a cubic active pocket with a side length of 30 angstroms was constructed with the action position in step (3) as the center for virtual screening of drugs;

[0032] (5) using Sybyl-X software, converting the pharmacophore model selected in step (1) into a three-dimensional query structure, and performing a preliminary screening of an organic compound database based on the three-dimensional query structure to obtain drug molecules that meet the query structure;

[0033] (6) using Autodock software to perform molecular docking on the drug molecules screened out in step (5) in the active pocket region obtained in step (4), completing the secondary screening of the organic compound database and obtaining the molecular docking energy of the drug-coal active site; sorting the drug molecules by the molecular docking energy, and screening 10 molecules with larger energy;

[0034] (7) performing a laboratory unit flotation test on the reagent molecules selected in step (6); then performing data processing on the flotation results, calculating the combustible recovery rate corresponding to each flotation reagent, and determining the reagent with the highest combustible recovery rate as the template molecule;

[0035] (8) using the combustible recovery rate of the template molecule determined in step (7) as the performance index of each agent molecule, and constructing a three-dimensional quantitative structure-activity relationship model using the least squares method;

[0036] (9) The three-dimensional quantitative structure-activity relationship model constructed in step (8) was cross-validated by the leave-one-out method to obtain the cross-validation coefficient q 2 The consistency of the three-dimensional quantitative structure-activity relationship model constructed in step (8) was tested by non-cross-validation method to obtain the standard deviation SEE and Fischer test value F of the three-dimensional quantitative structure-activity relationship model. 2 , SEE value and F value, evaluate the robustness and predictability and select the best 3D-QSAR model; the 3D-QSAR model includes a comparative molecular field analysis method model and a comparative molecular similarity index analysis method model; the comparative molecular field analysis method model is the sum of one or both of the steric field and the electrostatic field, and the comparative molecular similarity index analysis method model is the sum of one or more of the steric field, the electrostatic field, the hydrophobic field, the hydrogen bond donor field and the hydrogen bond acceptor field; when selecting the best 3D-QSAR model, q 2 The best value is the one with the largest value and 0.5 as the qualified boundary, the best value is the one with the largest value and 100 as the qualified boundary, and the best value is the one with the smallest SEE value;

[0037] (10) Using Sybyl-X software, a three-dimensional isopotential map of the 3D-QSAR model is drawn. The optimization information of the template molecule proposed by the three-dimensional isopotential map is analyzed by comparing the effect of the drug. Based on the analysis results, the drug molecule is selected.

[0038] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A computer-aided coal flotation reagent virtual screening and design method, characterized in that: The following steps are involved: (1) Using Sybyl-X software, a series of coal flotation agent molecules with the same parent structure are flexibly superimposed in the internal space to generate multiple conformations, and the conformations generated by each compound are rigidly superimposed to generate multiple pharmacophore models, and the appropriate pharmacophore model is selected according to the performance indicators of the pharmacophore; (2) Obtain 1.3kg / cm through a small floating test 3 The low-ash coal was obtained, and the structural information of the obtained low-ash coal was detected. The coal molecular plane structure was constructed based on the detected information. The coal molecular plane structure was corrected using ACD / CNMR predictor software, and the coal molecular structure was optimized using Material Studio software to obtain the coal 3D molecular minimum energy conformation. (3) Drawing a series of coal flotation agent molecules with the same parent structure using Material Studio software, constructing an agent-coal interaction system together with the coal molecules optimized in step (2), performing molecular dynamics simulation on the system, and recording the positions of the interaction between the two; (4) Using Autodock software, a cubic active pocket is constructed with the action position in step (3) as the center for virtual screening of drugs; (5) using Sybyl-X software, converting the pharmacophore model selected in step (1) into a three-dimensional query structure, and performing a preliminary screening of an organic compound database based on the three-dimensional query structure to obtain drug molecules that meet the query structure; (6) using Autodock software to perform molecular docking on the drug molecules screened out in step (5) in the active pocket region obtained in step (4), completing the secondary screening of the organic compound database and obtaining the molecular docking energy of the drug-coal active site; sorting the drug molecules by the molecular docking energy, and screening 10 to 15 drug molecules with larger energy; (7) performing a laboratory unit flotation test on the reagent molecules selected in step (6); then performing data processing on the flotation results, calculating the combustible recovery rate corresponding to each flotation reagent, and determining the reagent with the highest combustible recovery rate as the template molecule; (8) using the combustible recovery rate obtained in step (7) as the performance index of each agent molecule, and constructing a three-dimensional quantitative structure-activity relationship model using the least squares method; (9) The three-dimensional quantitative structure-activity relationship model constructed in step (8) was cross-validated by the leave-one-out method to obtain the cross-validation coefficient q 2 The consistency of the three-dimensional quantitative structure-activity relationship model constructed in step (8) was tested by non-cross-validation method to obtain the standard deviation SEE and Fischer test value F of the three-dimensional quantitative structure-activity relationship model. 2 , SEE value and F value, evaluate the robustness and predictability and select the best 3D-QSAR model; (10) Using Sybyl-X software, draw the three-dimensional isopotential map of the 3D-QSAR model, analyze the structural optimization information of the template molecule proposed by the three-dimensional isopotential map, and select the drug molecule based on the analysis results.

2. A computer-aided coal flotation reagent virtual screening and design method as claimed in claim 1, characterized in that: The performance indicators of the pharmacophore model in step (1) are lower Energy value, higher Specificity, Sterics, H-bond and Mol-Qry values.

3. A computer-aided coal flotation reagent virtual screening and design method as claimed in claim 1, characterized in that: The side length of the active pocket of the cube in step (4) is 30 angstroms.

4. A computer-aided coal flotation reagent virtual screening and design method as claimed in claim 1, characterized in that: The 3D-QSAR model in step (9) includes a comparative molecular field analysis method model and a comparative molecular similarity index analysis method model.

5. A computer-aided coal flotation reagent virtual screening and design method as claimed in claim 4, characterized in that: The comparative molecular field analysis method model is the sum of one or both of the steric field and the electrostatic field, and the comparative molecular similarity index analysis method model is the sum of one or more of the steric field, the electrostatic field, the hydrophobic field, the hydrogen bond donor field and the hydrogen bond acceptor field.

6. A computer-aided coal flotation reagent virtual screening and design method as claimed in claim 1, characterized in that: When selecting the best 3D-QSAR model in step (9), q 2 The best value is the one with the largest value and 0.5 as the qualified boundary, the best value is the one with the largest value and 100 as the qualified boundary, and the best value is the one with the smallest SEE value.