Aquatic species nano-silver toxicity prediction method based on multiple linear regression

Abstract

The invention discloses an aquatic species nano-silver toxicity prediction method based on multiple linear regression, and the method comprises the following steps: collecting the physical property, chemical property, experimental method and environmental condition data of nano-silver, and obtaining a data set comprising a descriptor of nano-silver and aquatic species toxicity data; for missing values existing in the data set, preprocessing the data, performing normalization processing on the preprocessed data, and dividing the data set into a training set and a verification set; constructing a multi-species nano-silver toxicity prediction model, and training the multi-species nano-silver toxicity prediction model by using the training set data until the fitting of the multi-species nano-silver toxicity prediction model is stable; and inputting the verification set data into the multi-species nano-silver toxicity prediction model, and outputting a nano-silver toxicity value by the multi-species nano-silver toxicity prediction model for evaluating the nano-silver ecological risk. The toxicity of the nano-silver is rapidly predicted, the method is simple and rapid, the cost is low, and manpower, material resources and financial resources required by experimental determination are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-throughput ecological risk assessment of nanomaterials, and in particular to a method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression for ecological risk assessment. Background Art

[0002] The aquatic toxicity assessment of artificial nanomaterials mainly follows the experimental methods of chemical toxicity assessment. For example, the standard OECD (Organization for Economic Co-operation and Development) ecotoxicity test methods include algal growth toxicity, large Daphnia acute toxicity and fish acute toxicity. Nanomaterials are defined as materials with one or more external dimensions between 1nm and 100nm. Due to the nano-size effect, nanomaterials have many unique physical and chemical properties, such as large specific surface area and high surface energy.

[0003] These properties not only bring many excellent performances to nanomaterials, but are also important factors affecting the toxicity of nanomaterials. Nanomaterials are not exactly the same as traditional chemicals. For example, silver (Ag) nanomaterials cannot be regarded as silver (Ag) for toxicity assessment, because changes in physical and chemical properties such as size, shape, surface modification or coating can affect the toxicological effects of silver (Ag). It is difficult to complete the toxicity assessment of all forms of artificial nanomaterials using only experimental methods. A faster and more effective method is needed to conduct toxicity assessment of artificial nanomaterials.

[0004] Computational toxicology methods, such as quantitative structure-activity relationship (QSAR) models, are widely used in the toxicity assessment of chemicals. The QSAR model constructed for nanomaterials is called the quantitative nanostructure-activity relationship (Nano-QSAR) model. Similar to QSAR, Nano-QSAR also follows the assumption that similar nanomaterials may produce similar biological effects. The construction of Nano-QSAR models relies on a set of descriptors that encode the composition, topology or geometric characteristics of nanomaterials. These descriptors can be obtained either through customized software or through experimental testing, such as elemental composition, Zeta potential, size distribution, shape, etc.

[0005] Machine learning methods are an indispensable analytical tool in computational toxicology. In recent years, with the development of artificial intelligence, machine learning algorithms have been increasingly applied to various aspects. However, for machine learning models, it is a black box model that can neither explain the mechanism nor the existing machine learning models are mostly built for a single species and cannot predict the toxicity of multiple species at the same time. Therefore, it is necessary to establish a nanosilver toxicity prediction model that has good prediction effects, can be used for mechanism explanation, and can predict the toxicity of multiple species at the same time. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and provide a method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression. A multiple linear regression algorithm is used to construct a multi-species nanosilver toxicity prediction model for the ecological risk assessment of nanosilver.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression comprises the following steps:

[0009] S1. Collect data on the physical properties, chemical properties, experimental methods, and environmental conditions of nanosilver, and obtain a dataset including descriptors of nanosilver and toxicity data for aquatic species;

[0010] S2. Preprocess the data for missing values ​​in the data set, normalize the preprocessed data, and divide the data set into a training set and a validation set;

[0011] S3. Construct a multi-species nanosilver toxicity prediction model, and train the multi-species nanosilver toxicity prediction model using the training set data until the multi-species nanosilver toxicity prediction model is fitted stably;

[0012] S4. The validation set data is input into the multi-species nanosilver toxicity prediction model, and the multi-species nanosilver toxicity prediction model outputs the multi-species nanosilver toxicity prediction value for evaluating the ecological risk of nanosilver.

[0013] Further, the descriptors of nanosilver include: source, shape, surface coating, diameter, size test method, hydrated size, solubility, zeta potential, polymer dispersibility index, pH, temperature, light, exposure duration, exposure experiment, and algae growth status.

[0014] Furthermore, the data is preprocessed for missing values ​​in the data set, including:

[0015] For numerical data, the mean value is used to replace the missing values ​​of the corresponding descriptors;

[0016] For text description data, one-hot encoding is used to convert the text description data into binary numerical data, that is, convert it into 1 and 2. If there are missing values, the number 3 is used to fill them.

[0017] Furthermore, diameter, hydrated size, solubility, zeta potential, polymer dispersibility index, pH, temperature, light, and exposure time are numerical data.

[0018] Furthermore, the source, shape, surface coating, size test method, exposure experiment, and algae growth status are textual description data.

[0019] Furthermore, the normalization formula is:

[0020]

[0021] In the formula, x norm represents the normalized value, x represents the normalized value, x max Indicates the maximum value of the descriptor column, x min Indicates the minimum value of the descriptor column.

[0022] Furthermore, the normalized data set is divided into a training set and a validation set in a ratio of 85:15, where the training set contains 162 data and the validation set contains 29 data.

[0023] Furthermore, a multivariate linear regression algorithm was used to construct a multi-species nanosilver toxicity prediction model, which is:

[0024] Log 10 (EC 50 )=-1.16×origin-0.89×shape-0.34×coating+0.20×diameter

[0025] +0.68×method-0.53×size-1.18×dissolution-1.86×zeta+0.06×PDI-1.10×organism+0.65×pH+1.81×temp-0.20×illumination+0.23×duration+4.43

[0026] In the formula, origin means source; shape means shape; coating means surface coating; diameter means diameter; method means size test method; size means hydration size; dissolution means solubility; zeta means Zeta potential; PDI means polymer dispersibility index; organism means species; pH means acidity and alkalinity; temp means temperature; illumination means light; duration means exposure time.

[0027] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression described above are implemented.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] 1. The present invention can quickly predict the toxicity of nanosilver algae, large Daphnia and fish through the physical properties, chemical properties, quasi-experimental methods and environmental condition data of nanosilver, and because of the specific formula, it can also provide reliable mechanism revelation for the prediction. The method is not only simple, fast and low-cost, but also saves the manpower, material and financial resources required for experimental determination.

[0031] 2. The establishment and verification of the multi-species toxicity prediction method of nanosilver of the present invention are strictly in accordance with the OECD guidelines for the establishment and use of QSAR models. Therefore, the use of the multi-species nanosilver toxicity prediction results of the present invention can provide data support for chemical regulation and is of great significance to the ecological risk assessment of chemicals. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Flow chart of the method for predicting the toxicity of nanosilver to aquatic species based on multivariate linear regression.

[0033] Figure 2 Schematic diagram of the performance characterization of the multi-species nanosilver toxicity prediction model. DETAILED DESCRIPTION

[0034] The method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0035] See also Figure 1The present invention discloses a method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression, comprising the following steps:

[0036] S1. Collect data on the physical properties, chemical properties, experimental methods, and environmental conditions of nanosilver, and obtain a dataset including descriptors of nanosilver and toxicity data for aquatic species;

[0037] S2. Preprocess the data for missing values ​​in the data set, normalize the preprocessed data, and divide the data set into a training set and a validation set;

[0038] S3. Construct a multi-species nanosilver toxicity prediction model, and train the multi-species nanosilver toxicity prediction model using the training set data until the multi-species nanosilver toxicity prediction model is fitted stably;

[0039] S4. The validation set data is input into the multi-species nanosilver toxicity prediction model, and the multi-species nanosilver toxicity prediction model outputs the multi-species nanosilver toxicity prediction value for evaluating the ecological risk of nanosilver.

[0040] The present invention obtains a data set including nanosilver descriptors and aquatic species toxicity data through the physical properties, chemical properties, experimental methods and environmental conditions of nanosilver. A multi-species nanosilver toxicity prediction model for algae, large daphnia and fish is constructed using a multivariate linear regression algorithm. The multi-species nanosilver toxicity prediction model can provide data support for the ecological risk assessment of nanomaterials.

[0041] Specifically, the descriptors of nanosilver include: source, shape, surface coating, diameter, size test method, hydrated size, solubility, Zeta potential, polymer dispersity index (PDI), pH (Pondus Hydrogenii), temperature, light, exposure time, exposure experiment, algae growth state. Zeta potential refers to the potential of the shear plane, also known as electromotive potential or electromotive potential (ζ-potential or ζ-potential), which is an important indicator to characterize the stability of colloidal dispersions.

[0042] Specifically, for the missing values ​​in the data set, the data is preprocessed, including:

[0043] For numerical data, the average value is used to replace the missing value of the corresponding descriptor; for text description data, one-hot encoding is used to convert the text description data into binary numerical data, that is, convert it into 1 and 2. If there are missing values, the number 3 is used to fill them.

[0044] Among them, diameter, hydrated size, solubility, Zeta potential, polymer dispersibility index, pH, temperature, light, and exposure time are numerical data. Source, shape, surface coating, size test method, exposure experiment, and algae growth status are textual description data.

[0045] Since the model was constructed using a multivariate linear regression algorithm, in order to avoid the large difference in magnitude between descriptors that would affect the final model form, the data was first scaled to the range of [0,1] using a normalization method. The maximum and minimum values ​​of the nanosilver descriptors during normalization are shown in Table 1.

[0046] The normalization formula is:

[0047]

[0048] In the formula, x norm represents the normalized value, x represents the normalized value, x max Indicates the maximum value of the descriptor column, x min Indicates the minimum value of the descriptor column.

[0049] Table 1 Maximum and minimum values ​​of nanosilver descriptors when normalized

[0050] Descriptors Minimum Maximum source 0 3 shape 0 3 Surface coating 0 3 diameter 1.3 456 Dimensional test methods 0 3 Hydration size 2.36 9023 Solubility 0.001726 49 Zeta -257 42 PDI 0.19 1.2 Species 0 2 pH 5.3 9 temperature 16 37 illumination 0 24 Exposure duration 0.25 600

[0051] The normalized data set is divided into a training set and a validation set in a ratio of 85:15, where the training set contains 162 data and the validation set contains 29 data.

[0052] The multivariate linear regression algorithm was used to construct a multi-species nanosilver toxicity prediction model. The multi-species (algae, large Daphnia and fish) nanosilver toxicity prediction model was as follows:

[0053] Log 10 (EC 50 )=-1.16×origin-0.89×shape-0.34×coating+0.20×diameter

[0054] +0.68×method-0.53×size-1.18×dissolution-1.86×zeta+0.06×PDI-1.10×organism+0.65×pH+1.81×temp-0.20×illumination+0.23×duration+4.43(2)

[0055] In the formula, origin means source; shape means shape; coating means surface coating; diameter means diameter; method means size test method; size means hydration size; dissolution means solubility; zeta means Zeta potential; PDI means polymer dispersibility index; organism means species; pH means acidity and alkalinity; temp means temperature; illumination means light; duration means exposure time.

[0056] See also Figure 2 , the goodness of fit R of the training set 2 tr =0.27, goodness of fit R of the validation set 2 test =0.28, mean square error MSE of the training set tr =1.39, mean square error MSE of the validation set test =1.18, indicating that the multi-species nanosilver toxicity prediction model has a certain goodness of fit and external prediction ability, and can be used for nanosilver ecological risk assessment.

[0057] Example

[0058] Given a nanosilver material, it is assumed that its source is commercial, the surface is coated, the shape is spherical, the diameter is 15 nanometers, the hydrated diameter test method is dynamic light scattering (DLS), the hydrated diameter is 2.36 nanometers, the solubility is 5%, the zeta potential is -26mV, the polymer dispersion index is 0.34, the pH is 7.32, the exposure temperature is 24°C, the illumination time is 24 hours, and the exposure time is 72 hours.

[0059] First, after normalization, the normalized value is calculated according to the maximum and minimum values ​​of the nanosilver descriptors in Table 1 and the normalization formula (1). Then, the normalized value is substituted into the formula (2) of the multi-species nanosilver toxicity prediction model to calculate the toxicity value EC 50 The logarithmic size of .

[0060] In summary, the present invention has the following advantages and beneficial effects:

[0061] 1. The present invention can quickly predict the toxicity of nanosilver algae, large Daphnia and fish through the physical properties, chemical properties, quasi-experimental methods and environmental condition data of nanosilver, and because of the specific formula, it can also provide reliable mechanism revelation for the prediction. The method is not only simple, fast and low-cost, but also saves the manpower, material and financial resources required for experimental determination.

[0062] 2. The establishment and verification of the multi-species toxicity prediction method of nanosilver of the present invention are strictly in accordance with the OECD guidelines for the establishment and use of QSAR models. Therefore, the use of the multi-species nanosilver toxicity prediction results of the present invention can provide data support for chemical regulation and is of great significance to the ecological risk assessment of chemicals.

[0063] The present invention also discloses an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression as described above are implemented. The electronic device of the present invention can execute the method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0064] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression as described above are implemented. The computer-readable storage medium of the present invention can execute the method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0065] Although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the system disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0066] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0067] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0068] The various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0069] The above description is a detailed description of the preferred feasible embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modified changes completed under the technical spirit disclosed by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression, characterized in that: The following steps are involved: S1. Collect data on the physical properties, chemical properties, experimental methods, and environmental conditions of nanosilver, and obtain a dataset including descriptors of nanosilver and toxicity data for aquatic species; S2. Preprocess the data for missing values ​​in the data set, normalize the preprocessed data, and divide the data set into a training set and a validation set; S3. Construct a multi-species nanosilver toxicity prediction model, and train the multi-species nanosilver toxicity prediction model using the training set data until the multi-species nanosilver toxicity prediction model is fitted stably; S4. The validation set data is input into the multi-species nanosilver toxicity prediction model, and the multi-species nanosilver toxicity prediction model outputs the multi-species nanosilver toxicity prediction value for evaluating the ecological risk of nanosilver.

2. The method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression according to claim 1, characterized in that: Descriptors of nanosilver include: source, shape, surface coating, diameter, size test method, hydrated size, solubility, zeta potential, polymer dispersibility index, pH, temperature, light, exposure duration, exposure experiment, and algal growth status.

3. The method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression according to claim 2, characterized in that: According to the missing values ​​in the data set, the data is preprocessed, including: For numerical data, the mean value is used to replace the missing values ​​of the corresponding descriptors; For textual description data, one-hot encoding is used to convert the textual description data into binary numerical data, that is, convert it into 1 and 2. If there are missing values, the number 3 is used to fill them.

4. The method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression according to claim 3, characterized in that: Diameter, hydrated size, solubility, zeta potential, polymer dispersibility index, pH, temperature, light, and exposure time are numerical data.

5. The method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression according to claim 3, characterized in that: The source, shape, surface coating, size test method, exposure experiment, and algae growth status are textual description data.

6. The method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression according to claim 1, characterized in that: The normalization formula is: In the formula, x norm represents the normalized value, x represents the normalized value, x max Indicates the maximum value of the descriptor column, x min Indicates the minimum value of the descriptor column.

7. The method for predicting the toxicity of nanosilver in aquatic species based on multivariate linear regression according to claim 6, characterized in that: The normalized data set is divided into a training set and a validation set in a ratio of 85:15, where the training set contains 162 data and the validation set contains 29 data.

8. The method for predicting the toxicity of nanosilver in aquatic species based on multiple linear regression according to claim 1, characterized in that: The multivariate linear regression algorithm was used to construct a multi-species nanosilver toxicity prediction model, which is: Log 10 (EC 50 )=-1.16×origin-0.89×shape-0.34×coating+0.20×diameter+0.68×method-0.53×size-1.18×dissolution-1.86×zeta+0.06×PDI-1.10×organism+0.65×pH+1.81×temp-0.20×illumination+0.23×duration+4.43 In the formula, origin means source; shape means shape; coating means surface coating; diameter means diameter; method means size test method; size means hydrated size; dissolution means solubility; zeta means Zeta potential; PDI stands for polymer dispersibility index; organism stands for species; pH stands for acidity; temp stands for temperature; illumination stands for light; and duration stands for exposure time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for predicting the toxicity of nanosilver to aquatic species based on multivariate linear regression are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the toxicity of nanosilver to aquatic species based on multivariate linear regression are implemented as described in any one of claims 1 to 8.

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