161 results about "Molecular property" 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 provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention discloses a molecular property prediction method and device based on a graph neural network and a large language model, and the method comprises the steps: obtaining a molecular structure graph according to the SMILES of a molecule, and the molecular structure graph comprises the structural information of atoms and functional groups; generating a description text of each functional group by using the fine-tuned large language model, and encoding the description text by using a text encoder to obtain text information of each functional group; adding text nodes and feature information in the molecular structure diagram, and adding edges between the text nodes and corresponding functional groups to obtain a final edition molecular structure diagram; and inputting the final edition molecular structure diagram into the trained molecular property predictor to obtain a prediction result of the molecular property. According to the method, structural information and text information of atoms and functional groups in molecules are fully considered, and then the information is learned by using the message passing neural network, so that molecular property prediction is more accurate, and the accuracy of molecular property prediction is improved.

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 embodiment of the invention discloses a training method, a prediction method, a device and equipment for a molecular attribute prediction model, and relates to the field of artificial intelligence assisted pharmacy. Comprising the steps that in a first training stage, in order to improve the interpretability of a sub-graph extraction model, the sub-graph extraction model and a sub-graph prediction model are trained based on a sample molecular graph, and the sub-graph extraction model obtained through training is used for extracting at least two interpretable key sub-graphs from the molecular graph; and in a second training stage, on the basis of the sub-graph extraction model obtained by training in the first training stage, training the sub-graph prediction model based on the sample sub-graph by taking improvement of the distribution external generalization ability of the sub-graph prediction model as a target, the sub-graph prediction model obtained through training is used for predicting molecular attributes of the molecular graph based on the at least two key sub-graphs of the molecular graph. By adopting the method provided by the invention, the interpretability of molecular attribute prediction and the generalization ability outside distribution can be 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 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.

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 relates to a molecular property prediction method and system based on multi-task pre-training and multi-modal fusion. The method comprises the following steps: collecting a data set and performing molecular conversion; performing multi-task pre-training, which comprises the following steps: generating a heterogeneous enhanced view; constructing a pseudo label; performing comparative learning according to the structure enhanced view and the heterogeneous enhanced view, and constructing a maximum similar task; capturing semantic differences among molecules according to the pseudo labels; carrying out multi-modal fusion, namely introducing functional group structure information, and extracting molecular sequence characteristics based on Transform and Mamba2 to obtain fused molecular multi-modal representation; and analyzing and predicting the classification or regression task. A heterogeneous enhanced view is established in a multi-task pre-training stage, multi-task self-supervision is performed in combination with multi-granularity features of molecular fingerprints, and functional group structure information is introduced in a multi-modal fusion stage, so that deep cross-modal interaction is realized, downstream prediction performance is improved, and candidate drug screening and molecular property evaluation processes are accelerated.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for valuing and or authenticating property. An example system includes a sensor system and a computer system. The sensor system includes one or quantum sensors, and is configured to generate first sensor data representing one or more molecular properties of a first object at a first time. The computer system is configured, at least, to: receive the first sensor data from the sensor system; obtain model data representing one or more molecular properties of a model object; generate, based on the first sensor data and the model data, a comparison between the one or more molecular properties of the first object at the first time and the one or more molecular properties of the model object; and determine an authenticity of the first object based on the comparison.

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.

This invention relates to a method and apparatus for predicting molecular properties by integrating three-dimensional structure and prior features. The method includes: identifying a set of predicted properties and a set of prior properties; constructing a molecular property prediction model based on the predicted and prior property sets; constructing a model training dataset and training the molecular property prediction model based on the dataset; after model training, receiving first molecular information input by the user, preparing model input data based on the first molecular information and the prior property set, and inputting the three-dimensional molecular structure M and prior characteristic vector X obtained from this data preparation into the molecular property prediction model to predict the corresponding property prediction vector Y, which is then fed back to the current user. This invention can reduce prediction complexity, shorten prediction time, and improve prediction efficiency.

The invention provides a molecular property prediction method and system, computer equipment and a storage medium, and belongs to the field of compound intelligent analysis, and the method comprises the steps: obtaining an SMILES character string of a to-be-recognized molecule; converting the character string to obtain a molecular object, extracting a fingerprint vector from the molecular object, and performing linear transformation processing to obtain chemical substructure features; constructing a two-dimensional molecular map based on the molecular object, and extracting features of the two-dimensional molecular map to obtain local topological structure features; rendering the molecular object to generate a molecular image and extracting image features; performing feature splicing on the chemical substructure features, the local topological structure features and the image features to obtain fusion features; mapping the fusion feature into an output space to determine a predicted value, and generating a molecular property prediction result of the to-be-identified molecule based on the predicted value. According to the method, complementation among different information types can be realized, more information of molecules can be explored from different angles, and the accuracy and the universality of predicting molecular properties are enhanced.

The invention relates to the technical field of artificial intelligence drug research and development, in particular to a molecular ADMET property prediction algorithm based on multi-mode and multi-scale characteristics. According to the algorithm, multi-scale feature extraction is carried out on a small molecule SMILES character string by constructing a multi-modal feature fusion framework, and training and prediction are completed on 73 ADMET properties covering absorption, distribution, metabolism, excretion, toxicity and general characteristics. The invention provides a multi-modal and multi-scale feature fusion strategy, atomic features, molecular fingerprint features and physicochemical property features are integrated, and a multi-scale feature set covering molecules is formed. A progressive information extraction architecture is combined with a graph neural network (GCN), a gated loop unit (GRU) and an attention mechanism to extract features, so that the accuracy of ADMET property prediction is remarkably improved. In a word, the method marks an important step for predicting the ADMET property of the medicine.

The invention discloses a molecular multi-modal representation learning method, device and equipment and a storage medium, and relates to the technical field of molecular representation learning, and the method comprises the steps: obtaining the respective multi-modal data of each sample molecule and the real label data of each sample molecule; inputting the multi-modal data into the contrast learning model in batches to obtain predicted label data of the target data; and under the condition that the comparative learning model does not meet a preset training termination condition, optimizing the comparative learning model based on the predicted label data and the real label data to obtain a new comparative learning model, and returning to execute the step of inputting the multi-modal data into the comparative learning model in batches. And carrying out molecular multi-modal representation learning based on the contrast learning model until the contrast learning model meets a training termination condition. The accuracy of the comparative learning model for molecular property prediction can be improved.

The invention relates to a multi-model-based petrochemical production stream molecular-level composition prediction method and system, and relates to the technical field of oil refining chemical molecule management, and the method comprises the steps: measuring the molecular composition of a raw material, splicing structural units based on a structural unit-bond electric matrix frame, and constructing a molecular digital structure topology; constructing a molecular composition model by combining a preset molecular database, a probability density function and a molecular property predictor; establishing a molecular-level reaction network based on a catalytic reforming reaction mechanism, and constructing a molecular-level reaction kinetic model in combination with a reaction rate expression; constructing a reactor model according to mass, energy and momentum transfer equations; inactivation and separation unit models are constructed, multiple models are coupled in sequence, a whole-process reaction-separation molecular level prediction model is generated, and any stream molecular level composition is predicted. The method solves the problem that traditional petrochemical production depends on measurement results to adjust process parameters and has operation lag, and can eliminate lag and improve production control precision by predicting stream molecular composition.

A method for computational drug design includes defining a population of a plurality of compounds. Each compound includes one or more molecular properties. The method includes defining a training set of compounds from the population for which one or more biological properties are known. The method includes selecting, from the population, a subset of one or more compounds that are not in the training set. The method includes determining a subset score of the selected subset based on molecular properties of the one or more compounds in the selected subset, and evaluating the selected subset based on the determined subset score. The subset score is determined based on a frequency of the molecular properties in the population and on a frequency of the molecular properties in a sampled set comprising the training set and the selected subset.

The invention provides a molecular property prediction method and device based on chemical bond characterization learning. The method comprises the following steps: carrying out standardization processing on simplified molecule linear input standard data of a target molecule to obtain standardized data of the target molecule; based on a predetermined key classification standard, the standardized data is converted into chemical bond characterization data, and the predetermined key classification standard is used for classifying chemical bond features; and predicting the chemical property of the target molecule based on the chemical bond characterization data to obtain a prediction result.

Embodiments of the present disclosure provide a molecular property prediction method, related device and medium. The method splits and recombines a first molecule in an unlabeled molecule dataset to obtain a new second molecule, determines a training sample based on the first molecule and the second molecule, and optimizes a molecular encoder using the training sample. Then, the molecular property prediction model based on the optimized molecular encoder is trained using a labeled molecule dataset to achieve accurate prediction of the molecular property. Embodiments of the present disclosure can fully utilize the substructure information inside the molecule to improve the accuracy and generalization ability of the prediction, optimize the molecular representation ability of the molecular encoder, and enable the target molecular encoder to better understand the combination relationship between the molecular fragments, thereby improving the representation quality and prediction accuracy of complex molecular structures. Embodiments of the present disclosure can be applied to drug discovery, material science, molecular virtual screening and other scenarios.

The present invention discloses a method for predicting small-sample molecular properties based on a hybrid relational network. The method comprises the following steps: 1) processing data to generate multiple subtasks, with each task having an equal number of positive and negative samples in its support set; 2) constructing a two-dimensional molecular relational graph and a molecular distribution relational graph based on property perception, and then using the two molecular relational graphs to construct a hybrid relational network module; 3) using the true labels of molecular properties and molecular topological fingerprint knowledge based on chemical domain knowledge to construct a hybrid prior relational graph to constrain the optimization of the hybrid relational network module, thereby effectively achieving label propagation. Compared with existing technologies, the present invention utilizes the knowledge contained in different relational graphs to effectively improve the prediction effect of the model in small-sample molecular property prediction tasks.

The present invention discloses a model training method and a method and apparatus for predicting molecular characteristic information, the method comprising the steps of acquiring data on a specified proteolysis-inducing chimeric molecule, constructing three-dimensional molecular graph data of the specified proteolysis-inducing chimeric molecule based on the data, inputting the three-dimensional molecular graph data of the specified proteolysis-inducing chimeric molecule into a prediction model to be trained and having the prediction model predict molecular characteristic information of the specified proteolysis-inducing chimeric molecule, and training the prediction model based on the difference between the predicted molecular characteristic information and the actual molecular characteristic information corresponding to the specified proteolysis-inducing chimeric molecule.

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.

The invention relates to the field of artificial intelligence, in particular to a molecular property prediction method, system, device and equipment, and the method comprises the steps: firstly obtaining 3D molecular structure data containing atomic coordinate information; according to the set radius alpha and the filling value sigma, zero-dimensional, one-dimensional and two-dimensional simplex structures are extracted, and substructure position coding information is calculated; then, taking one-dimensional and two-dimensional simplex structures as atomic feature indexes, and fusing atomic topology and atomic features; on the basis of zero-dimensional simplex structure and substructure position coding, obtaining pairing position coding information of an atom-simplex pair; inputting the fused atomic features and pairing position coding information into a multi-head self-attention module to generate global molecular features; and finally, obtaining a molecular property prediction result according to the global molecular characteristics. According to the method, the defect of processing fine-grained substructure information in the prior art can be effectively solved, and the 3D molecular property prediction performance is greatly improved.

The present invention discloses a method for constructing a large language model for predicting molecular properties based on a knowledge graph. The method includes multiple steps: first, high-throughput data acquisition technology is used to obtain pre-training corpus, including relevant data such as molecular properties and metabolic pathways. Then, based on these data, a knowledge graph about fourteen endpoints is constructed, and the knowledge graph represents the relationship between molecules, their properties and endpoints in the form of triples. Next, the large language model is pre-trained using LoRA technology, and the model is optimized using an incremental learning strategy to form an expert large model. Subsequently, RAG technology is used to retrieve triplets related to the target endpoint from the knowledge graph, and the retrieval results are sorted. Through prompt word design, the expert large model filters the retrieved triplets and extracts key information. Finally, the filtered triple list is converted into natural language input to the expert large model to generate prediction results of molecular properties and provide a basis for reasoning. This method significantly improves the accuracy and efficiency of predicting chemical reactions between molecules and endpoints.

The application discloses a molecular property prediction method based on spectral position coding and bionic side inhibition gating, and relates to the field of olfactory perception. The method comprises the following steps: constructing a molecular structured input comprising atomic types, a Coulomb matrix and graph Laplacian eigenvector; fusing atomic chemical attributes and spectral domain topological positions through a feature extraction module to generate initial atomic features; using a multi-scale aggregation module, hierarchical neighborhood aggregation is carried out based on preset scale constraints to extract multi-level structure features covering chemical bonds, functional groups and molecular skeletons; with the help of a global interaction module of bionic side inhibition gating, local features are dynamically modulated by a global query signal to realize adaptive denoising and semantic sharpening; finally, through mask pooling and a decoupled multi-label prediction head, the scores of each odor attribute of the molecule are output. The application significantly improves the accuracy, structure perception ability and cross-task generalization performance of molecular property prediction.

This platform integrates machine learning models with traditional mathematical optimization to efficiently solve complex, multi-objective problems in polymer discovery. Machine learning models, such as neural networks, generate initial polymer structures by modeling complex, non-linear relationships between various molecular properties. These initial structures are then refined through mathematical optimization techniques to ensure they meet specific constraints and performance criteria related to the desired molecular properties. This hybrid approach accelerates polymer development across various applications, including drug delivery, sustainable materials, and advanced technologies, offering tailored solutions that optimize performance, environmental sustainability, and efficiency.