128 results about "Molecular descriptor" patented technology

The invention relates to the technical field of drug research and development, and discloses a drug-like molecule screening system and method based on deep learning, and the system comprises a physical and chemical characteristic analysis module which is used for calculating molecular descriptors of compounds and generating a visual characteristic distribution chart; and the drug-like molecule screening module integrates a Lipinski rule, a Veber rule, a Ghose rule and a Muegge rule, and supports a user to customize a screening rule. Compound characteristics are comprehensively analyzed through the physical and chemical characteristic analysis module, flexible screening is achieved through the drug-like molecule screening module, toxicity risks are accurately recognized through the toxicity assessment module, the affinity assessment module is combined to assess the binding capacity in multiple ways, and the synthesis feasibility assessment module plans an efficient and low-cost synthesis path. The dynamic feedback optimization module continuously improves the system performance; all the modules work cooperatively, the problems of scattered tools, insufficient precision, low planning efficiency, optimization deficiency and the like in the prior art are effectively solved, the medicine research and development efficiency is greatly improved, and the research and development risk and cost are reduced.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention discloses a rare and endangered organism toxicity prediction method based on a machine learning algorithm and a quantitative structure-activity relationship, which constructs a toxicity prediction model through the machine learning algorithm, can effectively assess the toxicity influence of environmental pollutants on rare and endangered organisms, and provides technical support for rare and endangered organism protection and ecological environment risk assessment. Comprising the following steps: 1, collecting data related to rare and endangered organisms from a public database, and establishing a rare and endangered organism toxicity prediction database based on machine learning; 2, generating molecular descriptors for the chemical substances; step 3, data preprocessing; 4, development of acute and chronic toxicity prediction models of rare and endangered organisms based on multiple machine learning is carried out, and performance evaluation is carried out; 5, performing internal and external verification on the rare and endangered biotoxicity prediction model; step 6, analyzing the importance of the features by using the valuable, rare and endangered biotoxicity prediction model, and finding out the most influential features; and 7, predicting the toxicity value of the pollutants in combination with the optimal machine learning model.

Disclosed are systems and methods for identifying a target molecule by receiving a molecule dataset from a library of molecules comprising chemical-structural representations of candidate molecules for a material application; determining one or more target molecular property values derived from a set of predictive molecular descriptor values determined from a candidate molecule of the molecular dataset applied to a trained machine learning model, wherein the trained ML model is configured to output the set of predictive molecular descriptor values for a given molecule data input, and wherein the trained ML model was trained for a set of training data and the set of molecular descriptors generated from a molecular descriptor modeling application; and outputting, via a report or to a data store, the one or more target molecular property values for each of the molecular dataset.

The invention discloses a meta-learning-based small sample pharmacokinetic property prediction method and related equipment, and relates to the field of pharmacokinetics. The method comprises the following steps: acquiring multi-dimensional feature data of candidate compounds, wherein the multi-dimensional feature data comprises atomic-scale features, molecular descriptors and molecular structure features; inputting the multi-dimensional feature data into a preset pharmacokinetic property prediction model to obtain a preliminary prediction result, the pharmacokinetic property prediction model being pre-trained through meta-learning based on small samples, the model comprising a first machine learning model and a second machine learning model, the preliminary prediction result comprises a first prediction result output by the first machine learning model and a second prediction result output by the second machine learning model; and determining a pharmacokinetic property prediction result of the candidate compound according to the first prediction result and the second prediction result. According to the method, the problem of low accuracy of a pharmacokinetic property prediction result can be relieved in a small sample scene of early drug research and development.

The present invention provides a technology for predicting the level of PPO inhibition objectively and with high accuracy using LUMO distribution as a "molecular descriptor," and a technology for selecting a PPO inhibitor based on the predicted level of PPO inhibition. [Solution] The present invention provides a method for predicting PPO inhibitory activity based on the correlation between LUMO distribution and PPO inhibitory activity for each compound, predicting the level of PPO inhibitory activity based on the correlation between the difference in variation in PPO inhibitory activity between different plant species and the level of PPO inhibitory activity, and selecting PPO inhibitors using the predicted PPO inhibitory activity as an index.

The invention discloses a pre-training model and multi-source coding feature fusion-based water pollutant molecular property prediction method and system, and relates to the technical field of environmental science and artificial intelligence crossing. Comprising the steps of collecting pollutant data; the collected pollutant data is preprocessed; taking SMILES as a molecular input basis, and extracting molecular features; according to the molecular features, feature vectors are obtained from an encoder and then input to a vector interaction module for feature fusion; and embedding the comprehensive molecules obtained after feature fusion into an expert mixed structure module to obtain comprehensive representation of an expert mechanism, inputting the comprehensive representation of the expert mechanism into a preset prediction head, and completing prediction of molecular properties through the prediction head. According to the method, the molecular language model features, the molecular graph neural network features and the molecular descriptor features are integrated, and feature fusion is carried out by utilizing an attention mechanism and residual connection, so that high-precision prediction of the molecular properties of pollutants is realized.

The invention discloses a method for predicting molecular solvation free energy based on machine learning, relates to the technical field of computational chemistry, and solves the technical problem that a molecular dynamics method consumes a large amount of computing resources and time. The method comprises the following steps: using SMILES formulas of solute molecules and solvent molecules as input data; converting the SMILES formula into a one-dimensional descriptor, a two-dimensional descriptor and a three-dimensional descriptor of a molecule based on an RDKit tool; based on an open source experiment data set, training a machine learning model through an association relationship between a one-dimensional descriptor, a two-dimensional descriptor and a three-dimensional descriptor of a molecule and experiment solvation free energy to obtain a prediction model; and inputting molecular descriptors of solute molecules and solvent molecules into the prediction model to obtain a result of predicting solvation free energy of the solute molecules. Existing molecular dynamics simulation is replaced by the machine learning model, and meanwhile, model training is performed by adopting a machine learning algorithm and experimental data, so that the accuracy and the stability of a prediction result are improved.

The invention discloses a reverse molecular design method and system of an organic framework material COFs based on a machine learning and quantitative calculation technology, and relates to the technical field of material science and computer simulation. The photoelectric property of the COFs material can be accurately predicted, and a new material can be directionally synthesized under guidance. A COFs structure database is established, Gaussian is used for representing the fragment structure and electronic characteristics of the COFs, a molecular descriptor and a machine learning algorithm are used for establishing a structure-performance mapping relation, quantitative calculation software is used for obtaining an important target quantity for describing the photoelectric characteristics of the COFs, SHAP is used for carrying out importance analysis, and finally a machine learning model for predicting the photoelectric properties of the COFs is obtained. According to the model, through theoretical prediction of a novel COFs structure, a mapping relation between the COFs structure and photoelectric specificity can be established, and a novel COFs hypothetical structure can be reset according to important molecular fragments or word structures of the important molecular fragments; theoretical guidance can be provided for application in the fields of photoelectric catalysts and the like.

The invention relates to the technical field of new pollutant risk identification, in particular to a new pollutant multi-medium PNEC predictive analysis system and method based on machine learning, and the method comprises the steps: collecting a compound SMILES structure and fresh water PNEC, BCF and Koc data thereof; using tools such as RDKit, rcdk and the like to automatically calculate molecular descriptors; key features are screened through redundant feature processing and a random forest algorithm; an RF model, an XGBoost model, a LightGBM model and a CatBoost model are respectively constructed for PNEC prediction, and a BCF model and a Koc model are constructed by adopting the LightGBM; and a visual platform is built based on an R language Shiny framework, so that full-process automation of SMILES input, model prediction and result display is realized. The system provides a convenient tool for multi-medium PNEC prediction, has the advantages of high efficiency, accuracy, low cost and the like, is suitable for large-scale new pollutant environmental risk identification, and has wide application prospects and important environmental protection significance.

The invention discloses a method for predicting liquid chromatography retention time of traditional Chinese medicine active ingredients. The method comprises the following steps: detecting retention time of flavonoid compounds or anthraquinone compounds under different chromatographic conditions through a reverse high performance liquid chromatograph; performing structure optimization on the flavonoid compound or the anthraquinone compound; performing molecular descriptor calculation on the optimized compound structure to obtain a molecular descriptor data set; performing genetic algorithm screening on the molecular descriptor data set to obtain characteristic molecular descriptors; combining the characteristic molecule descriptors with the different chromatographic conditions into a complete data set; the complete data set serves as an input variable, retention time serves as an output variable, and a gradient elevator GB algorithm or a random forest method RF is adopted for modeling to obtain a QSRR model; and predicting the retention time of flavonoid or anthraquinone compounds under different chromatographic conditions by using the constructed QSRR model.

The invention provides a method for predicting the flammability upper limit volume percentage of a pure compound. The method can be used for predicting a mathematical model of the flammability upper limit volume percentage of the pure compound which is composed of 12 or less elements of hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, arsenic and the like and has the atom number of 25 or less (excluding hydrogen) with high accuracy. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a step-by-step selection method, and the model takes some of the various molecular descriptors as independent variables, takes flammability upper limit volume percent as a dependent variable, and takes the flammability upper limit volume percent as a dependent variable. The value of the molecular descriptor included in the model can be received and input in a short time, and the flammability upper limit volume percentage can be output, so that only the specific value of the molecular descriptor included in the model is known. And the flammability upper limit volume percentage of the compound purely formed by the molecule can be predicted for any molecule meeting the requirements of the invention.

Provided is a mathematical model that can predict, with high accuracy, the saturated liquid density of a pure compound at 298.15 K, said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic and having 25 or less atoms (excluding hydrogen). The model is a quantitative structure-property relation model and is obtained by solving an optimal model from a plurality of multiple linear regression models by a step-by-step selection method, and the model takes some molecule descriptors in various molecule descriptors as independent variables, takes saturated liquid density under 298.15 K as a dependent variable, and takes the saturated liquid density under 298.15 K as a dependent variable. The value of the molecule descriptor included in the input model can be received in a short time, and the saturated liquid density at 298.15 K can be output, so that the saturated liquid density at 298.15 K of a compound formed by the molecule singly can be predicted for any molecule meeting the requirements of the invention as long as the specific value of the molecule descriptor included in the model is known.

The invention relates to the technical field of drug research and development, and discloses a drug intermediate database construction and AI intelligent retrieval method. The method comprises the following steps: acquiring a chemical structure and synthesis path data of a target drug intermediate, extracting a molecular descriptor by utilizing a preset feature extraction model, and constructing a structured intermediate database according to the molecular descriptor; and inputting the database into an AI retrieval model based on a graph neural network algorithm to generate a dynamic retrieval result, performing multi-dimensional sorting by using a preset optimization algorithm, and outputting a standardized retrieval list. The method further comprises the steps of feature extraction model training, data preprocessing, retrieval result verification, data security control and the like. According to the method, multi-source data can be efficiently integrated, key information can be accurately extracted, intelligent retrieval is achieved, data safety is guaranteed, the medicine research and development efficiency can be remarkably improved, the research and development cost is reduced, and powerful support is provided for medicine research and development.

The invention relates to the technical field of electrical data processing, and provides an anthocyanin stabilizer screening method and system based on fusion of quantum chemistry calculation and machine learning. The method comprises the following steps: constructing a molecular structure data set; performing geometric structure optimization on the anthocyanin molecules and the candidate polyphenol molecules to obtain a ground state optimization structure; performing multi-dimensional molecule descriptor calculation on the candidate polyphenol molecules to obtain a polyphenol molecule descriptor matrix; performing structure optimization, single-point energy calculation and free energy calculation on the anthocyanin polyphenol compound system to obtain a combined free energy value set; integrating the polyphenol molecule descriptor matrix and the combination free energy value set, constructing a training data set, and performing feature screening; performing model training through a machine learning algorithm to obtain a combined free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol compound through the binding free energy prediction model to obtain candidate molecules of the anthocyanin stabilizer. The screening efficiency of the anthocyanin stabilizer is improved.

The present invention discloses a drug molecular property prediction and classification method and system based on BiLSTM; wherein the method includes: obtaining a drug molecule to be predicted, and converting the molecular structure of the drug analysis to obtain a drug molecular descriptor and a drug molecular fingerprint; inputting both the drug molecular descriptor and the drug molecular fingerprint into a trained drug molecular property prediction and classification network, and outputting a drug molecular property prediction result; the working principle of the trained drug molecular property prediction and classification network includes: respectively extracting features from the drug molecular descriptor and the drug molecular fingerprint to obtain two drug molecular feature vectors; performing feature fusion on the two drug molecular feature vectors; performing classification prediction on the fused feature vectors, and outputting a drug molecular property prediction result.

To provide an insilico toxicity risk evaluation method in which high prediction accuracy is compatible with the explicitness of a prediction basis.SOLUTION: A machine learning model for predicting the intensity of the toxicity risk of a chemical substance by an information processing system is constructed using a molecular descriptor based on a chemical structure and inchemico or invitro test data, the prediction result of the toxicity risk intensity output by the information processing system by the machine learning model is used as an index of similarity specific to toxicity, and the toxicity risk is evaluated by a Reed-Acros method based on the similarity by the index.SELECTED DRAWING: Figure 1

A formulation graph convolution network (F-GCN) with multiple GCNs assembled in parallel and connected to filters and an external learning architecture is able to predict the effectiveness of a formulation. Input into the multiple GCNs are molecular structures of formulants, which are processed as molecular graphs and output as molecular descriptors. The molecular descriptors are filtered by normalized ratios or fractions of the ingredient molecules in a formulation, such as a battery electrolyte or solvent. A formulation descriptor combines the filtered molecular descriptors to arrive at a predicted performance for the formulation, such as the battery capacity for an electrolyte formulation, by an external learning architecture. F-GCN may use a pre-trained GCN with physico-chemical properties of known molecular structures.

The invention discloses a multi-label smell description prediction method, and relates to the field of compound smell prediction, and the method comprises the steps: obtaining compound identification information, molecular structure descriptors and smell label data, and constructing a multi-label smell data set; generating a molecular structure feature vector through a molecular fingerprint coding technology, and extracting a multi-dimensional descriptor reflecting the physicochemical properties of molecules; compressing the molecular fingerprint features to a low-dimensional space through a dimension reduction algorithm; performing unbalanced data processing on the training set, fusing the dimension-reduced molecular fingerprints with the molecular descriptors to form a joint feature matrix, and configuring a class weight balance mechanism and overfitting suppression parameters by adopting a multi-label classification architecture; independently optimizing a probability threshold for each odor label based on the verification set; and outputting a multi-odor label combination prediction result according to the target molecule identification information. According to the scheme, the multi-odor characteristics of the compound can be accurately depicted, and the combined recognition accuracy of the compound odor is remarkably improved.

The invention discloses a dynamic prediction chloropropanol ester omics analysis method. The method comprises the following steps: S1, constructing a molecular descriptor matrix of glyceride of a chloropropanol ester seed structure and a three-dimensional information base of chloropropanol ester; s2, establishing a computer simulation algorithm of a synthetic reaction of chloropropanol ester in the sample; s3, taking glyceride as a seed structure, and establishing a chloropropanol mass spectrum recognition database; and S4, identifying chloropropanol ester omics in the sample by using a threshold score. According to the method, a liquid chromatography-mass spectrometer is used for constructing a molecular descriptor matrix of different types of chloropropanol esters, and the range of the chloropropanol esters is greatly expanded through a computer simulation algorithm of a synthetic reaction of the chloropropanol esters in a sample. According to the method, basic information units are expanded on a computer in a mechanism-driven manner to realize the identification of chloropropanol esters, mass spectrum information is deeply annotated to re-characterize functional groups, the mass spectrum information is fully utilized, the identification accuracy of chloropropanol esters is ensured, and finally, the identification of different types of chloropropanol esters in a sample is realized.

The invention discloses a new pollutant identification method, system and device, and a storage medium. The new pollutant identification method comprises the following steps: acquiring a multi-modal feature of a to-be-identified pollutant; wherein the multi-modal feature comprises at least one of a first molecular descriptor feature, a first molecular structure image feature and a first text feature; inputting the multi-modal features into a trained modal detection and completion model for modal completion to obtain second molecular descriptor features, second molecular structure image features and second text features; fusing the second molecular descriptor feature, the second molecular structure image feature and the second text feature to obtain a fused feature; and inputting the fused features into the trained new pollutant identification model for new pollutant identification to obtain a classification result of the to-be-identified pollutants, and improving the accuracy of new pollutant type identification by fusing the structured data, the image data and the semantic text data.

The invention discloses an ionic liquid screening method and device and application thereof in traditional Chinese medicine volatile oil extraction, and belongs to the technical field of volatile oil extraction. The method comprises the following steps: calculating three-dimensional molecular descriptors of various ionic liquid samples; constructing a multi-module partial least square prediction model according to the three-dimensional molecule descriptors of the plurality of ionic liquid samples and the extraction performance indexes of the ionic liquid samples; inputting multiple ionic liquids to be screened, calculating three-dimensional molecular descriptors of the multiple ionic liquids to be screened, and screening out the ionic liquid with the highest extraction performance index according to a prediction result of the multi-module partial least square prediction model. The problems that in the prior art, ionic liquid screening needs to be conducted through a large number of experiments, the experiment cost is high, and a large amount of time is consumed are solved.

The invention provides a method for predicting a flash point of a pure compound by using a multiple linear regression model. According to the method, a mathematical model for predicting the flash point of the pure compound with high accuracy is established to predict a flash point value. Specifically, the model is used as a quantitative structure-property relationship model, after the optimal model is obtained from a plurality of multiple linear regression models by using a stepwise selection method for most compounds with known flash point experimental values, the molecular descriptors included in the model are received in a short time, and the flash point values are output. As long as the specific values of the molecule descriptors included in the model are known, the flash point of the compound composed of the molecule is predicted for any molecule. As described above, the present invention provides a method and model capable of predicting a reliable flash point value even for many compounds having unknown experimental values, thereby saving the cost and time for physical performance measurement experiments or chemical structure prediction, and enabling research and development activities of related industries to become easier.

This invention discloses a high-trophic-level food chain biomagnification prediction model for organic chemicals. The model comprises the following steps: S1. Dividing the sample into six training samples and three validation samples; S2. Retaining a 1169-dimensional molecular descriptor matrix after screening; S3. Generating an extended pollutant parameter database by combining laboratory biomagnification factors; S4. Forming a position-time normalized sample dataset; S5. Obtaining a fused amplification probability result set; S6. Identifying a candidate set of pollutant amplification paths on the coupled directed graph of amplification probability-flux-stability; S7. Performing uncertainty propagation and confidence assessment on the candidate set of pollutant amplification paths, outputting a priority control list of pollutant amplification paths and their confidence information. This invention addresses the problems of insufficient model coverage and weak extrapolation ability caused by traditional methods relying solely on single molecular descriptors or experimental values.

The present invention discloses a system and method for predicting the absorption spectrum of probe molecules targeting pathogenic proteins, belonging to the field of protein detection. The present invention obtains multiple groups of probe molecule-pathogenic protein complex conformations; divides each group of conformations into regions, calculates and obtains the absorption wavelength and intensity of different excited states of the probe molecules; counts the internal coordinates of the probe molecules after removing hydrogen atoms in the conformation, uses bond length, bond angle, and dihedral angle as molecular descriptors and also as characteristic variables, and uses absorption wavelength and intensity as output, divides the data into training sets and test sets, and builds a fully connected neural network for machine learning; uses the absorption wavelength and intensity of different excited states as the horizontal and vertical coordinates, respectively, to draw the absorption spectrum. The present invention helps researchers accurately and efficiently predict the absorption spectrum of biomarker probe molecules, reduces the time cost, labor cost, and testing cost of measuring the absorption spectrum of probe molecules targeting pathogenic proteins, and is expected to be applied to medical care, biopharmaceuticals and other fields.

The invention discloses a melting point prediction method for a nitrate-based fused cast explosive. The method comprises the following steps: S1, constructing a data set matched with a nitrate-based energetic material; s2, constructing a user-defined molecular descriptor set of the melting points of the nitrate-based energetic materials; s3, based on the data set, optimizing the LightGBM, XGBoost and BPNN machine learning models by adopting an ablation experiment method and a training experiment, and obtaining an optimal model for predicting the melting point of the nitrate-based energetic material by comparing model evaluation indexes and errors; and S4, converting the newly designed nitrate-based energetic material I into a corresponding user-defined molecular descriptor set I, and taking the user-defined molecular descriptor set I as the input of the prediction machine learning model to obtain the output value of the melting point Tm. The method for predicting the melting point of the nitrate-based fused cast explosive, provided by the invention, has good prediction accuracy and interpretation capability, and can be used for predicting the performance of nitrate-based energetic materials.

The invention discloses chenopodium quinoa willd source ACE inhibitory peptides IDL and VSF based on virtual screening and enzyme digestion matching and application of the chenopodium quinoa willd source ACE inhibitory peptides IDL and VSF, and belongs to the technical field of bioactive peptides. According to the novel ACE inhibitory peptide screening method, the molecular descriptor, the scoring function and the interaction information descriptor information of the peptide are combined, computer simulation screening is adopted, and the screening speed and accuracy of the ACE inhibitory peptide are improved. The ACE inhibitory peptide obtained based on the ACE inhibitory peptide screening method disclosed by the invention has a very strong inhibitory effect on ACE, and is strong in thermal stability, wide in pH stability range and excellent in ion stability. The compound has practical application value, and has good application potential in development of antihypertensive ACE inhibition drugs.

The present application relates to the technical field of electric data processing, and provides a screening method and system for anthocyanin stabilizer based on quantum chemistry calculation and machine learning fusion. The method comprises the following steps: constructing a molecular structure dataset; performing geometric structure optimization on anthocyanin molecules and candidate polyphenol molecules to obtain ground state optimized structures; performing multidimensional molecular descriptor calculation on the candidate polyphenol molecules to obtain a polyphenol molecule descriptor matrix; performing structure optimization, single-point energy calculation and free energy calculation on the anthocyanin polyphenol complex system to obtain a binding free energy numerical set; integrating the polyphenol molecule descriptor matrix and the binding free energy numerical set, constructing a training dataset and performing feature screening; performing model training through a machine learning algorithm to obtain a binding free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol complex through the binding free energy prediction model to obtain candidate molecules for anthocyanin stabilizer. The present application improves the screening efficiency of anthocyanin stabilizer.

The application discloses a method for predicting active structures interfering with biological pathways, which comprises the following steps: constructing a compound biological pathway interference database containing clear labels such as cell lines, exposure time and exposure concentration; evaluating the consistency of the biological pathway cross degree and the regulation trend of the training set and the test set through cumulative hypergeometric distribution and cumulative Bernoulli distribution; identifying a batch of potential compounds in the training set; evaluating the occurrence frequency of the molecular descriptors in the potential compounds through cumulative distribution probability; and finally predicting the potential active structures driving the change of the biological pathway through inputting the biological pathway.

The present invention relates to the field of drug research and development technology, and discloses a drug intermediate database construction and AI intelligent retrieval method. The chemical structure and synthesis path data of the target drug intermediate are obtained, and the molecular descriptors are extracted using a preset feature extraction model, and a structured intermediate database is constructed accordingly. The database is then input into an AI retrieval model based on a graph neural network algorithm to generate dynamic retrieval results, and a preset optimization algorithm is used for multi-dimensional sorting, and a standardized retrieval list is output. The method also includes the steps of feature extraction model training, data preprocessing, retrieval result verification, and data security control. The present invention can efficiently integrate multi-source data, accurately extract key information, realize intelligent retrieval, and ensure data security, which can significantly improve drug research and development efficiency, reduce research and development costs, and provide strong support for drug research and development.