187 results about "Molecular graph" patented technology

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention relates to the technical field of molecular property prediction, in particular to a molecular property prediction method based on double-graph collaborative representation and cross-view feature fusion, which comprises the following steps: automatically identifying key functional groups in a molecular graph through a graph attention mechanism (GAT), and constructing a Motif graph to represent a local chemical environment of the molecular graph; the Motif graph can effectively capture local structure characteristics in molecules. The Motif graph and the molecular graph are respectively processed through GAT, information of global features and local features is respectively extracted, and the two features are fused through cross attention to obtain fused molecular graph features, so that more comprehensive molecular feature representation is obtained, and the comprehensiveness of molecular representation and the performance of the model are improved. Molecular fingerprint information is jointly processed by using a bidirectional gating cycle unit (Bi-GRU) and a multi-head attention network, the expression ability of the model to complex molecular fingerprint data is enhanced, and the accuracy of molecular property prediction and the generalization ability of the model are improved.

The invention discloses a contrastive learning molecular property prediction method based on an anti-fact and a large language model, and belongs to the technical field of molecular representation learning combining a large language model and a graph neural network, and the method comprises the following steps: generating an original molecular graph through a molecular SMILES character string; generating a skeleton disturbance diagram and a functional group disturbance diagram; ensuring that the generated perturbation diagram is an anti-fact hard negative sample; generating a molecular natural language description through a large language model, and converting the molecular natural language description into a molecular semantic embedding vector by using a small language model; performing comparative learning among the original molecular graph, the positive sample and the anti-fact hard negative sample; original molecular graph embedding and molecular semantic embedding obtained after comparative learning are fused, and downstream molecular property prediction is carried out. According to the method, hard negative samples can be generated through an anti-fact mechanism, the diversity of molecular characterization modes can be ensured through a large language model, and the accuracy of comparative learning during molecular property prediction is remarkably improved.

A molecular structure prediction method based on a multi-granularity graph neural network comprises the steps of data set preprocessing, multi-granularity level data feature construction, multi-granularity graph neural network construction, multi-granularity graph neural network training, multi-granularity graph neural network verification and multi-granularity graph neural network testing. The key connection relation between atoms, the incidence relation of substructure keys and graph-level global feature representation are determined in the molecular graph, and the problem of local information loss in graph representation learning is effectively relieved; a message passing mechanism of graph neural sub-networks with different granularities is improved, a multi-granularity molecular graph data structure is trained, unique information of each hierarchical molecular structure is fully utilized, and the modeling capability of a model for a complex chemical structure is enhanced. The method has the advantages of being high in prediction accuracy, reducing errors, relieving local information loss existing in the prediction method, being good in prediction interpretability and the like, and can be applied to the technical fields of drug discovery, molecular property prediction and the like.

The invention relates to a semi-template drug molecule inverse synthesis prediction method based on graph-sequence pre-training. The method comprises the following steps: selecting a public chemical molecule data set N; selecting a molecular sample from the N, and then extracting a molecular graph and a word segmentation sequence from the molecular sample; obtaining a sequence embedding matrix and a graph embedding matrix of the i by utilizing the word segmentation sequence of the i and the molecular graph; constructing an overall model M and a loss function Ltotal used for training the M; and training M in two stages by using AdamW to obtain a trained model M. By adopting the method disclosed by the invention, reactant atoms for generating a product can be accurately reduced, a starting material of the product is defined, and the research and development success rate of medicines or other products is improved.

The invention discloses a molecular performance prediction method and system based on layered characterization, and belongs to the field of molecular property prediction. The method comprises the following steps: constructing a multi-layer graph structure, including an atomic layer, a group layer and a molecular layer, based on a molecular graph structure and an SMILES sequence; designing a hierarchical information interaction mechanism combining intra-layer polymerization and inter-layer information transfer, and performing multilayer structure semantic fusion to obtain characterization of an atomic layer, a group layer and a molecular layer; a hierarchical graph representation fusion module is constructed based on an attention mechanism, differential fusion is carried out on representation of an atomic layer, a group layer and a molecular layer, expression of key structure features is automatically enhanced, and unified fusion molecular representation is constructed; according to the method, a prediction task-oriented loss function is constructed, fusion molecular representation is input into a full-connection neural network prediction model, end-to-end molecular performance prediction is realized by training and learning a mapping relation between the fusion molecular representation and molecular properties, and classification and regression performance in a molecular property prediction task is remarkably improved.

The invention relates to an enzyme turnover number prediction method and system fused with multi-modal characteristics of an enzymatic reaction, and belongs to the technical field of bioinformatics. Comprising the following steps: respectively constructing an optimized protein pre-training model and an optimized chemical reaction pre-training model; extracting sequence features of a to-be-detected amino acid sequence by utilizing the optimized protein pre-training model to obtain an enzyme sequence feature matrix; extracting sequence features of an enzymatic reaction SMILES sequence to be detected by utilizing the optimized chemical reaction pre-training model to obtain an enzymatic reaction SMILES feature matrix; using molecular fingerprints to represent substrate and product molecular sets, and extracting a set feature matrix of the two molecular sets through a graph attention network; the method comprises the following steps: representing substrate and product molecule sets by using a molecular graph, and extracting internal feature matrixes of molecules in the two molecule sets through a graph isomorphic network; and carrying out feature enhancement on the obtained feature matrix so as to obtain an enzyme turnover number prediction value. The method improves the stability and precision of the prediction result, and has the advantages of low cost and short period.

The invention provides a molecular-graph-oriented primitive-level representation learning method and system, and the method comprises the steps: carrying out the noise addition and reconstruction of an original molecular graph based on a graph diffusion model, calculating an edge reconstruction error, and dividing the original molecular graph into a plurality of primitive sub-graphs according to the edge reconstruction error; constructing an atomic view and a primitive view, and encoding the atomic view and the primitive view by using a graph neural network encoder to obtain an atomic embedding matrix and a primitive embedding matrix; and constructing a positive and negative sample pair based on the atomic view and the primitive view, and optimizing an atomic embedding matrix and a primitive embedding matrix by adopting a Babontwin double-view contrast loss function to realize cross-granularity expression alignment. According to the method, molecular primitives are extracted in a self-supervised mode through the diffusion model, systematic defects of a traditional method in the aspects of cross-granularity structure modeling, semantic guidance contrast learning and multi-level expression fusion are overcome through atom-primitive double-view alignment, and the semantic completeness and task generalization ability of molecular expression are remarkably improved.

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 provides a method for constructing a drug interaction prediction model based on multi-view feature representation, and the method comprises the steps: obtaining the node embedding representation of each drug from a drug knowledge graph through a heterogeneous graph neural network, and obtaining a first feature vector of each drug pair; utilizing a graph attention neural network to obtain graph embedded representation of each drug from the drug molecular graph to obtain a second feature vector of each drug pair, and obtaining a third feature vector of each drug pair from the drug bipartite graph; the first feature vector, the second feature vector and the third feature vector of each drug pair serve as input, the real DDI of each drug pair serves as output, the prediction probability of the multi-layer perceptron is infinitely close to the real DDI of each drug pair by adjusting parameters of the multi-layer perceptron, and a drug interaction prediction model is obtained. According to the method, the distinction degree of the DDI features is improved by using multi-view feature representation, so that the model prediction robustness is enhanced, and meanwhile, the problem of overfitting when the DDI of a new drug is predicted is solved.

The invention belongs to the technical field of active site prediction, and particularly relates to an enzyme active site prediction method based on a graph neural network. In order to realize high-precision prediction of active sites, the enzyme active site prediction model HFGN adopts a double-branch architecture: enzyme branches integrate a three-dimensional structure, PLM embedding, homology score and EC function annotation, and realize multi-source feature fusion through an attention mechanism; the reaction branch is based on molecular maps of a substrate and a product, chemical reaction specificity is modeled through a map neural network, and finally enzyme-reaction context sensing embedding is realized through a cross-modal attention mechanism, so that the prediction precision and generalization ability of residue-level active sites are improved.

The invention discloses a high-precision molecular property prediction method and device based on multi-modal information. The method comprises the following steps: acquiring a molecular map, a molecular sequence and a molecular three-dimensional geometric configuration of a molecule; decomposing each molecular graph into a group of motifs, constructing a multi-stage pre-training framework to capture multi-scale information in the molecule, and performing molecular graph training on the molecule to obtain a molecular graph feature vector; designing an information transmission mechanism based on distance and angle, and carrying out learning training on the molecular three-dimensional geometric configuration to obtain a molecular geometric feature vector; preprocessing the molecular sequence feature vector of the molecule, and learning the molecular sequence information of the molecule to obtain a molecular sequence feature vector; and according to the molecular map feature vector, the molecular geometric feature vector and the molecular sequence feature vector, designing a molecular property prediction model based on a cross-modal cross attention mechanism to perform molecular property prediction. The method can enhance the robustness and accuracy of molecular property prediction.

The invention provides a task processing method based on intermolecular interaction, a training method based on intermolecular interaction and a device thereof. Based on a connecting edge formed between a first node in the first molecular graph and a second node in the second molecular graph, respectively performing embedding feature representation on the first node and the second node to obtain a first node embedding feature and a second node embedding feature; identifying a first action node feature and a second action node feature; based on the first action node feature and the second action node feature, determining target substructures in chemical structures indicated by the first molecular graph and the second molecular graph respectively; the task matched with the first molecule and the second molecule is a regression task, and performing regression prediction on the target substructures corresponding to the first molecule graph and the second molecule graph to obtain a processing result; the task matched with the first molecule and the second molecule is a classification task, classification is carried out based on the target substructures corresponding to the first molecule graph and the second molecule graph, and a classification result is obtained.

The invention discloses a drug-target interaction prediction method and system, and belongs to the technical field of biological information. The method comprises the following steps: firstly, acquiring molecular structure data of a drug and sequence and structure data of a target spot; then, respectively extracting molecular map structure characteristics and SMILES sequence characteristics of the medicine, and amino acid sequence characteristics and three-dimensional space structure characteristics of a target spot; further, taking drugs and targets as nodes, taking the fused multi-modal features as node features, and combining known interaction and similarity information to construct an initial heterogeneous graph; inputting the heterogeneous graph into a dynamic graph neural network, dynamically learning an inter-node connection weight by using a graph attention mechanism, and iteratively updating node representation through multi-layer message transmission to obtain depth feature representation of drugs and targets; and finally, splicing the depth features, inputting the depth features into a multi-layer perceptron classifier, and predicting the drug-target interaction probability. According to the method, through multi-modal feature fusion and dynamic graph structure learning, the prediction accuracy and robustness are remarkably improved.

A computing system is provided, which receives a molecular graph at a message passing graph neural network (MPGNN), and produces scalar embeddings representing features of nodes and edges of the graph and vector embeddings representing geometric relationships of the graph. The system processes the scalar embeddings via a vector scalar interactive message passing mechanism of a message passing sub-block of theMPGNN to generate and pass scalar information from the scalar embeddings to an embedding space containing the vector embeddings. The system updates the vector embeddings based on the embedding space containing the scalar information and the vector embeddings. The system updates the scalar embeddings based on run-time geometry calculations of the geometric relationships encoded in the vector embeddings. The system computes an updated molecular graph based on the updated scalar and vector embeddings and outputs a target molecular property value based on the updated molecular graph.

The invention discloses an odor knowledge base construction method and system based on molecular modeling and deep learning, and belongs to the technical field of artificial intelligence and digital odor. According to the method, firstly, smell molecules are subjected to standardization treatment, and smell perception labels of the smell molecules are extracted to construct a smell knowledge base; expressing the odor molecules as a molecular graph structure, introducing molecular position coding to model a three-dimensional geometric configuration, and learning high-dimensional feature expression through a first graph neural network; then selecting odor molecules from the knowledge base and generating mixture candidates based on the similarity so as to form a mixture molecular map; and finally, processing the molecular map of the mixture by using a label prediction model, predicting an olfactory perception label of the mixture, and expanding a result to a knowledge base. According to the method, unified characterization of the single molecule and the mixture is realized, the technical vacancy of mixture olfaction prediction is filled, the dimensionality and expansibility of the knowledge base are improved, and the method can be widely applied to the fields of digital blending, smell recommendation and the like.

The invention belongs to the technical field of bioinformatics and deep learning, and particularly relates to a DNA methylation site prediction method, system and device and an electronic medium, and the method comprises the following steps: respectively coding single bases in a DNA sequence into four molecular fingerprints, and constructing each base into a molecular map structure; performing high-order feature extraction on the four molecular fingerprints through a multi-layer perceptron to generate molecular fingerprint features; modeling the molecular graph structure by adopting a multi-head graph attention network, calculating and normalizing attention scores among nodes, carrying out weighted summation on neighbor node features, and carrying out multi-head attention fusion to generate molecular graph features; splicing the molecular fingerprint features and molecular map features, and inputting the spliced molecular fingerprint features and molecular map features into a deep convolution gated channel attention module to generate fused features; and obtaining a prediction probability of the fused features, and judging whether the features are methylation sites or not according to the prediction probability. According to the invention, the identification capability of methylation sites can be improved.

The invention discloses a gas molecule smell prediction method and system based on an isotropic graph neural network, and relates to the technical field of tobacco production equipment.The method comprises the steps that an SMILES expression of gas molecules is converted into a molecular graph structure containing atomic features and edge features and three-dimensional space coordinates through DGL-LifeSci and RDKit toolkits; then inputting the data into an EGNN isotropic graph neural network, aggregating neighborhood information through a message passing mechanism, and synchronously updating node features and coordinate features; a plurality of EGNN layers are stacked for deep feature extraction, and high-dimensional node representation and final coordinate values are obtained; performing global pooling through an MLPReadout module to obtain a global molecular representation vector of a fixed dimension; and finally, mapping the vector to a 138-dimensional space through a full connection layer, processing through a Sigmoid function to obtain an odor probability vector, and finally judging and outputting a specific odor category through a threshold value.

The application discloses a kind of deep learning algorithm for predicting polypeptide in different blood stabilities, specifically includes: from public database systematically collects and organizes about polypeptide in human body, mouse body and in-vivo and in-vitro blood half-life experimental data and after strict data cleaning and verification process, construct high-quality data set as the basis of model training.Secondly create a multi-modal prediction model PepMSND, the model considers four key dimensional features of polypeptide, namely zero-dimensional polypeptide physicochemical property, one-dimensional polypeptide SMIELS code, two-dimensional polypeptide molecular graph, three-dimensional polypeptide 3D structure.In order to effectively process these different dimensional features, PepMSND integrates KAN, Transformer, GAT and SE (3) -Transformer and other advanced neural network components.The algorithm of the application provides an effective technical solution for the high cost and high time-consuming problem of plasma stability test in polypeptide drug research and development, thereby accelerating the early development process of polypeptide drug.

An input data generation system according to an embodiment includes at least one processor configured to accept input of at least first molecule graph data that specifies a molecular graph of a first molecule, second molecule graph data that specifies a molecular graph of a second molecule, and mixture ratio data that indicates a mixture ratio of the first molecule and the second molecule, combine the first molecule graph data and the second molecule graph data, generate synthetic molecule graph data, convert the synthetic molecule graph data into a feature vector, and generate input data for machine learning by reflecting the mixture ratio data in the feature vector.

The application provides a molecular graph output method and device. The method comprises the following steps: acquiring a chemical molecule SMILES; splitting the chemical molecule SMILES into a molecular substructure; inputting the chemical molecule SMILES and the molecular substructure into a pre-constructed molecular property prediction model; calling the molecular property prediction model to process the chemical molecule SMILES, obtaining a non-masked prediction value of the chemical molecule SMILES, and sequentially performing a masking process on the molecular substructure to obtain a masked prediction value of different molecular substructures; and outputting a molecular graph corresponding to the contribution of different substructures of the chemical molecule SMILES based on the non-masked prediction value and the masked prediction value. The application can calculate the contribution value of each substructure to the property and provide interpretability.

A foundation model for performing molecular-level tasks by learning multimodal data in the form of one-dimensional text and two-dimensional graphs, and a system therefor, according to an embodiment of the present invention, enable various molecular-unit tasks such as chemical reaction prediction, molecular attribute prediction, and natural language description generation to be effectively processed through a single foundation model. In addition, it is possible to increase prediction accuracy of the model by maximizing utilization of two-dimensional molecular graph information, and automatically generate, on the basis of statistical sparsity, high-quality descriptive text that emphasizes core and distinctive features of each molecule.

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 application discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and a Transformer, and relates to a drug synergistic effect prediction method. In order to solve the problem that a traditional drug combination discovery process mainly depends on clinical trials, which is not only time-consuming and laborious, but also high in cost and risky to patients, the application comprises the following steps: extracting drug data samples, generating a data set, dividing training and test sets, performing network training and testing; constructing a biomedical knowledge graph; converting a sequence list of a drug structure into a graph by using Rdkit; mining a subgraph of the knowledge graph by using a multi-hop subgraph mining network; learning feature representations of the knowledge network and the drug molecular graph by using a relation-aware Transformer, and performing fusion; performing synergistic effect prediction between drug pairs by using a multilayer perception machine; inputting drug pairs in a training set into the above model; inputting drug pairs in a test set into the prediction model to obtain a prediction result. The application belongs to the technical field of drug synergistic effect prediction.

This invention discloses a multimodal umami peptide identification method and system based on molecular graphs and sequence features, belonging to the interdisciplinary field of food bioinformatics and artificial intelligence. This invention extracts amino acid sequence features of peptides through a protein pre-trained language model, and simultaneously constructs a two-dimensional molecular graph based on the peptide's SMILES characterization to extract atomic-level structural features. Standard one-dimensional convolutional layers and dilated convolutional layers in a sequence encoder are used to capture local features and long-range dependency features, respectively, and atomic local structural features are extracted through a two-dimensional convolutional layer in a structural encoder. Subsequently, the extracted three types of features are pooled and concatenated, and an enhanced residual module is introduced to discriminate the peptide identification results. This invention overcomes the limitations of traditional methods that rely on single sequence features, and significantly improves the accuracy and interpretability of umami peptide identification by deeply fusing sequence semantics and molecular structural information.

The application discloses a compound water solubility prediction method based on enhanced molecular graph representation learning, and aims to improve the prediction accuracy and robustness. The technical scheme is: a compound water solubility prediction system based on enhanced molecular graph representation and double-branch graph neural network fusion is constructed, which is composed of a data preprocessing and enhanced molecular graph construction module and a water solubility prediction module (containing a double-branch fusion graph neural network model). The data set required for constructing the training prediction system is constructed. The double-branch fusion graph neural network model contains two branches of GraphSAGE and GIN, and the model is trained by using the AdamW optimization method combined with the learning rate scheduling strategy, gradient clipping and early stopping mechanism, and the trained prediction system is obtained by loading the model parameters with the best performance on the validation set. The trained prediction system is used to predict the compounds to be predicted, and the water solubility prediction value of the compound is obtained. The prediction accuracy of the application is significantly improved compared with the traditional method.

The application relates to the technical field of molecular property prediction and digital medical treatment, and discloses a molecular property prediction model training method, a storage medium, a computer device and a molecular property prediction device. The method comprises the following steps: acquiring molecular graphs of a plurality of sample molecules, splitting the molecular graphs to obtain candidate graph prompts, extracting a preset number of candidate graph prompts as target graph prompts according to a preset candidate graph prompt extraction rule; encoding the target graph prompts into target graph prompt vectors, calculating the importance weights of the target graph prompt vectors on the molecular graphs, calculating a global graph prompt vector according to the target graph prompt vectors and the importance weights; splicing the global graph prompt vector with feature vectors of the sample molecules respectively to obtain molecular vectors of the sample molecules; and training a molecular property prediction model according to preset molecular property labels corresponding to the sample molecules and the molecular vectors. The trained model can simultaneously predict a plurality of molecular properties, and the drug research and development efficiency is improved.