661 results about "Prediction score" patented technology

A multi-source data fusion method and apparatus, and an electronic device and a storage medium. The method comprises: taking multi-source data as different pieces of modal data; respectively constructing sub-hypergraphs; splicing the sub-hypergraphs into a hypergraph; then performing representation learning on a pre-constructed hypergraph neural network model according to the hypergraph, so as to obtain biological data node representations; and predicting an unknown or unobserved association relationship or interaction relationship according to the biological data node representations, so as to realize the construction of the relevance between biological nodes. The relevance makes data of different sources be fused together; the relevance is scored to obtain a prediction score, so as to determine the strength of the relevance; and finally, the hypergraph neural network model is repeatedly trained according to specific downstream tasks, and when a loss function converges, a satisfactory prediction result is obtained, such that fusion of multi-source data is realized.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age / time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encounters data records.

The invention is applicable to the technical field of biological information, and provides a drug target prediction method based on a graph neural network, which comprises the following steps of: respectively taking a drug and a target node as a core, and automatically extracting local sub-graphs of the drug and the target node to capture local context information of the nodes in a complex biological network; taking different types of edges as feature information and fusing the feature information into a feature learning process, dynamically adjusting contribution degrees of the different types of edges to node representation through an attention mechanism, introducing residual connection and a normalization processing strategy, and fusing node features; and performing deep mining on the fused node features by using a two-layer graph convolutional network, and calculating a potential interaction prediction score between the drug and the target. The invention further provides a drug target prediction model based on the graph neural network. According to the method, independent feature extraction and training are carried out on predicted drugs and target spots, and the accuracy and richness of node representation are effectively improved while the flexibility of the model is kept.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention discloses an lncRNA-miRNA association prediction method of a multi-channel attention mechanism, and relates to an lncRNA-miRNA association prediction method. The method aims to solve the problem that more effective and robust feature representation may not be learned due to the fact that feature representation of multiple data sources cannot be properly fused and similarity and difference between samples cannot be learned through a contrast learning method in an existing method. According to the method, multi-source data of lncRNA and miRNA is obtained, and multiple similarity matrixes and coding matrixes are obtained through calculation; in combination with a graph convolutional network and a Transform, capturing information from a local angle and a global angle; fusing feature representations of different data sources by using a multi-channel attention mechanism; a contrast learning method is introduced to further optimize feature representation, so that the consistency between different modal features of the same lncRNA or miRNA is ensured, and the discrimination capability of the model is enhanced; in the final prediction stage, KAN is utilized to obtain a prediction score associated with lncRNA-miRNA, and the parameter and calculation efficiency are further improved.

The invention discloses a user behavior sequence recommendation method and system based on the combination of a large model and a graph neural network, and a storage medium, relates to the technical field of behavior sequence recommendation, and aims to solve the problem that the text feature extraction capability of a large language model and the dynamic modeling capability of sequence recommendation are difficult to deeply fuse in an existing method. And accurate and comprehensive recommendation is difficult to realize. A large-model-based article text representation learning module of an LLM-SR model extracts high-quality semantic embedding of an article text through a large language model, and article representation adaptive to a recommendation task is generated in combination with linear layer mapping and a multi-layer perceptron; capturing a local sequence relation and a cross-session co-occurrence relation of the articles by utilizing the session graph and the global graph through a sequence representation learning module based on a graph neural network, and generating comprehensive representation of a user behavior sequence; and article scores are calculated according to the dot product of the user sequence representation and the candidate article embedding through a prediction score calculation module, and a final recommendation result is generated.

The invention relates to an intelligent lighting energy-saving method and system based on multi-modal data fusion. The method comprises the following steps: periodically obtaining a decision trajectory data set; combining the experience pool of the previous period with the decision trajectory data set newly collected in the current period according to a set proportion, and screening according to priority weight; training to obtain candidate strategies; evaluating the candidate strategies in a test scene set; adding the strategy comprehensive score into a strategy library when the strategy comprehensive score meets an access condition; when the strategy capacity in the strategy library reaches a preset value, eliminating the strategy with the lowest comprehensive strategy score based on the Pareto frontier; constructing a fitness function of the strategy and establishing a feedback-weight mapping model used for obtaining a prediction score; and based on the feedback-weight mapping model, establishing a loss function gradient, and based on the loss function gradient, performing iterated weight calculation to obtain iterated weights after the iterated calculation. According to the invention, the long-term energy-saving efficiency and the user experience of the lighting system are improved.

The invention discloses a regional document travel intelligent recommendation method, which comprises the following steps of: S1, collecting a large amount of data, and preprocessing the data; s2, constructing a user portrait by using a machine learning algorithm based on the behavior data of the user; s3, performing feature extraction on the data, processing text description related to the cultural resources by using a text mining technology, and extracting specified information; s4, designing a recommendation model, matching the user portrait with the text travel resource features, and calculating the preference degree of the user to the specific text travel resource; s5, generating a personalized regional travel recommendation result for the user by using the recommendation model, and selecting the most suitable recommendation result from the database of the travel resources according to the portrait and the historical behavior of the user; and S6, updating the user portrait and the recommendation model in real time, and providing a dynamic personalized recommendation result. According to the invention, the prediction score of the target user can be improved, and more accurate tourism place recommendation can be carried out on the target user.

The invention relates to the technical field of electric power system communication, in particular to an electric power 5G network service quality monitoring method and system and a storage medium, and the method comprises the steps: responding to a protocol data unit session establishment request signaling, and obtaining signaling plane data and data plane performance data through a signaling plane probe and a data plane probe; associating the signaling plane data with the data plane performance data through the service quality flow identifier and the timestamp to generate a multi-dimensional spatial-temporal characteristic matrix; based on the multi-dimensional spatio-temporal characteristic matrix, a network quality prediction score is obtained through dynamic spatio-temporal causality graph network prediction; and when the network quality prediction score exceeds a preset score threshold, establishing a network resource configuration optimization model to obtain an optimal network resource configuration strategy. According to the method, the signaling plane data and the data plane performance data are subjected to correlation analysis, and the network quality is predicted by using the dynamic space-time causal graph network, so that the network resource configuration is optimized, and the service quality of the power 5G network is ensured.

The invention relates to the technical field of artificial intelligence, in particular to a process reward model training method and system. The method comprises the following steps: calculating a confidence score of a question text in each sample; based on the correctness score, the confidence score and the tolerance distance hyper-parameter of each reasoning step of the problem text in the labeled sample, obtaining a target correctness score of each reasoning step of the problem text in the labeled sample; and training the process reward model based on binary cross entropy loss between the correctness prediction score of each reasoning step of the problem text in the labeled sample and the target correctness score, and taking the trained process reward model as a target process reward model. According to the method, the accuracy and reliability of process reward model training are improved, and the text generation precision of a large language model is enhanced.

The invention discloses a multi-modal robust recommendation method and system based on self-supervised learning, and the method comprises the steps: extracting the modal features of each modal of a user and an article from original data as initial embedded vectors, and constructing a user-article heterogeneous graph and an article-article isomerism graph under each modal; performing multi-modal feature coding on the initial embedded vector of each modal, and performing multi-modal feature fusion on the enhanced final embedded vector of each modal; and performing multi-modal feature alignment to extract a final user embedded vector # imgabs0 # and a final article embedded vector # imgabs1 #, calculating to obtain a final prediction score of the user on the articles, sorting the final prediction score, selecting specified first K articles as a recommendation list recommended to the user, and outputting the recommendation list. The invention aims to eliminate the influence of inherent noise in the multi-modal data and improve the recommendation effect of the whole multi-modal recommendation system.

The invention belongs to the technical field of intelligent traffic and automatic driving, and particularly relates to a pre-trained large language model-based vehicle trajectory prediction method, which comprises the following steps of: obtaining a space-time scene of a vehicle state and lane information; encoding the space-time scene of the vehicle state and the lane information, including extracting high-dimensional features through an embedded network, fusing the high-dimensional features to perform information exchange of the vehicle information and the lane information, and outputting sparse context joint codes; constructing a large language model; the sparse context joint coding obtains an advanced interaction state based on a pre-trained large language model; the motion and lane information of the target vehicle are synchronized through the Mama layer at each time step, prediction scores of a plurality of lane segments in the corresponding time step are obtained, the prediction score of the lane segment closest to the real track position is 1, and the prediction scores of the other lane segments are 0; the second input is processed by a multi-modal Gaussian decoder, resulting in a vehicle trajectory in consecutive time steps.

The invention provides a risk control cross-domain risk prediction method and system in combination with transfer learning, and the method comprises the steps: carrying out the domain difference measurement of a source domain and target domain risk control scene, generating a domain difference measurement matrix, building a hierarchical knowledge transfer channel based on the domain difference measurement matrix, and carrying out the prediction of the risk control cross-domain risk. And selectively migrating the source domain risk prediction model parameters, generating a target domain initialization risk prediction model, and carrying out dynamic adaptive training on the target domain initialization risk prediction model by adopting a target domain risk sample to obtain a cross-domain risk prediction model. And inputting the to-be-evaluated sample of the target domain into the cross-domain risk prediction model to generate a preliminary risk prediction score, and finally performing cross-domain calibration on the preliminary score based on the domain difference metric matrix to generate a final risk prediction result, thereby improving the accuracy of risk control cross-domain risk prediction.

The invention discloses an N-sugar chain marker combination for predicting the curative effect state of IgA and IgG type MM and a prediction scoring system and application of the N-sugar chain marker combination. The N-sugar chain marker combination is composed of the following 11 N-sugar chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb. The method has the advantages that (1) the relevance between the N-carbohydrate chain and the curative effect in the blood of the IgA and IgG type MM patients in different curative effect states is found for the first time, and the N-carbohydrate chain can be used as an index for predicting the curative effect states of the IgA and IgG type MM patients for the first time; and (2) the N-carbohydrate chain curative effect prediction scoring system provided by the invention is used for predicting the curative effect states of IgA and IgG type MM patients, and a new technical means is provided for predicting the curative effects of the IgA and IgG type MM patients.

The invention provides a voice keyword detection method, a storage medium and electronic equipment, and relates to the technical field of voice signal processing, and the method comprises the steps: carrying out the depth feature coding of a to-be-detected voice based on a plurality of cascaded coding layers in a CTC acoustic model; based on an intermediate decoding layer in the CTC acoustic model, decoding an intermediate coding feature output by the first coding layer to determine a first prediction score sequence for the target keyword; based on a final decoding layer in the CTC acoustic model, decoding depth coding features finally output by the cascaded coding layers to determine a second prediction score sequence for the target keyword; and calculating the similarity between the first prediction score sequence and the second prediction score sequence, and calibrating the second prediction score sequence based on the similarity to determine a target prediction score sequence corresponding to the target keyword. Therefore, through voice keyword detection with cross-layer discrimination consistency, a keyword detection effect with higher precision is realized.

The embodiment of the invention provides a display method and device for a control on a human-computer interaction interface and a storage medium, and belongs to the technical field of industrial control. The method comprises the following steps: acquiring target user information and interface use data of a currently logged-in target user; the interface use data represents use data generated by clicking and using each preset control on the human-computer interaction interface; inputting the target user information and the corresponding interface use data into a preset operation prediction model, and determining a prediction score of classifying each preset control into each preset display category; according to each prediction score and the current menu level of each control, determining a corresponding target menu path of each control; and according to the target menu path of each control, rendering each control in a preset display interface. According to the embodiment of the invention, the control layout on the human-computer interaction interface can be automatically adapted to the login user, so that the operation efficiency of the control on the human-computer interaction interface is improved.

The invention discloses a psychological counseling intelligent recommendation method and system based on artificial intelligence, and relates to the field of psychological counseling cross technology, and the method comprises the steps: calculating a three-dimensional emotion vector according to an emotion axis space and a syntactic structure, constructing a psychological semantic tensor by using a tensor outer product, generating a user intention tensor by using a prototype weight, and carrying out a flattening operation. Obtaining a final intention vector; combining the structural potential energy deviation and the spectral distance to construct a matching factor tensor; constructing a factorization machine model, predicting a matching score, generating a prediction score matrix, screening by using a Top-K selection method, generating recommended content, and constructing a visual interface to display the recommended content. According to the method, the fine granularity and accuracy of emotional representation are improved through emotional axis space and syntactic dependency analysis, the matching precision of intention and resources is improved through comprehensive calculation of semantic gravitation intensity and spectral distance, and the expression ability of matching factor tensor is enhanced through fusion of structural potential energy deviation and spectral distance.

The invention discloses an article aspect-level preference decoupling recommendation method based on big language model enhancement. The method comprises the following steps: 1, constructing a user-article interaction matrix based on scores and comment data of users; 2, utilizing a large language model and combining Prompt to extract aspect preferences of a user to an article and a user intention from the comments; 3, constructing a preference prediction model, decoupling the user-article interaction matrix into a sub-intention interaction matrix, and performing convolution operation on the sub-intention interaction matrix by using GCN to obtain user and article embedding under each intention; 4, using an attention mechanism to obtain user and article embedding of weighted fusion of all intentions; 5, calculating a prediction score; 6, constructing and optimizing a loss function; and 7, sorting the prediction scores in a descending order, and taking the first X items as a recommendation list. According to the method, user intentions are deduced from user comments by using a large language model, and user and article embedding for weighting and fusing all intentions is obtained in combination with a GCN and an attention mechanism, so that the recommendation precision can be improved.

The invention provides a human behavior recognition method based on fusion of multi-modal data acquired by an unmanned aerial vehicle, and the method comprises the following steps: splicing a joint Tokens with a space CLS Tokens of the same frame after feature extraction is completed by using a space Transform, and obtaining a feature fusion module; performing structure coding on the joint point sequence based on a human anatomy structure, and constructing a human topological structure association module; the method comprises the following steps of: constructing a time sequence cross Transform module; the space Transform, the feature fusion module, the human body topological structure association module and the time sequence crossing Transform module are stacked to obtain a trunk feature extraction network; a 10-layer trunk feature extraction network is adopted to perform deep feature extraction on input early-stage fusion data and feature fusion data, and classification is performed by using a classification head; the features output by the backbone network are mapped to the dimensions with the same number as the behavior categories, a prediction score is output for each category, and a human body behavior recognition result is obtained according to the prediction scores. According to the method, the human body behavior in the multi-modal data acquired by the unmanned aerial vehicle can be effectively identified.

The invention provides a speculation sampling method and device based on draft model token screening. The method comprises the following steps: a draft model performs reasoning calculation based on an input sequence to generate a plurality of candidate tokens; calculating a prediction score of each candidate token, wherein the prediction scores are used for representing the probability that the candidate tokens are accepted by the target model; screening the plurality of candidate tokens through the prediction score; inputting the screened candidate tokens into the target model for reasoning calculation again; and sampling a calculation result to generate a target token for subsequent reasoning calculation. According to the speculation sampling method and device based on draft model token screening, the sampling performance can be optimized while the reasoning result quality is ensured, the speculation sampling efficiency during large model reasoning optimization is effectively improved, and the application range is wide.

The invention is suitable for the technical field of computers, and provides a multi-dimensional scoring model training method, which comprises the following steps: inputting text data into a text graph model to obtain a training image; inputting the text data and the training image into a pre-training scoring model to obtain text embedding and image embedding; obtaining final image embedding based on text embedding and image embedding; based on the final image embedding, obtaining a prediction score of the training image; multi-dimensional score data of the training image is acquired, a score training loss function is generated based on the multi-dimensional score data, the prediction score and a preset parameter, and the preset parameter is a parameter specified by a user and represents the intervention degree of the user on the score; and training the pre-training scoring model based on the scoring training loss function to obtain a multi-dimensional scoring model. According to the method, the multi-dimensional score prediction of the training image can be realized, and the generated multi-dimensional score model can output the multi-dimensional comprehensive score based on the user opinion and the expert opinion.

Embodiments of the present disclosure provide a scoring model training method and apparatus, an image-text pair scoring method and apparatus, a device, and a medium. The method comprises: obtaining a sample image-text pair comprising a first image description of a first language, a sample image corresponding to the first image description, and a second image description of a second language, and label data of the sample image-text pair; using the first image description to obtain a first text representation by means of a first language encoder; using the second image description to obtain a second text representation by means of a second language encoder; using the sample image to obtain a sample image representation by means of an image encoder; using the first text representation, the second text representation, and the sample image representation to obtain a first multi-modal representation by means of a multi-modal fusion network; using the first multi-modal representation to obtain a prediction score by means of a scoring network; and using a difference between the prediction score and the label data to adjust a scoring model comprising the structure, so as to perform training to obtain a scoring model capable of performing quality evaluation on an image-text pair.

The invention relates to the technical field of computers, and discloses an account risk prediction method and system based on machine learning, and the method comprises the steps: obtaining transaction details, login behaviors and terminal environment information of a target account in different time periods, and constructing an account dynamic behavior feature data set; performing multi-layer nested feature extraction on the account dynamic behavior feature data set, and constructing an account risk behavior evolution model by adopting a time sequence convolutional network and attention mechanism fusion algorithm; judging whether the risk sensitivity of the account risk behavior evolution model in the current training period is stable or not based on the trend change of the response offset; aiming at the corrected behavior sequence, applying a multi-stage decision network and an anti-factual reasoning mechanism to generate a prediction score matrix of the potential risk account; and performing multi-dimensional fusion evaluation on the current state of the account according to a misjudgment boundary correction result in combination with a preset risk control knowledge base. The method has the advantage of reducing the misjudgment rate.

The invention discloses a colorectal cancer pathological image semi-supervised semantic segmentation method for regulating and controlling a pseudo label, and relates to the field of medical image visual segmentation. The method comprises the following steps: firstly, performing image acquisition and marking on a target pathological region, and dividing the target pathological region into a training set, a verification set and a test set in proportion; in the consistency regularization process of training the teacher model, sorting and reducing the categories of the low-confidence pseudo labels according to the confidence scores; the confidence coefficient of the pseudo-labels is evaluated through the prediction score difference between the final model and the optimal model of the trained teacher network, and the pseudo-labels with the high confidence coefficient are refined; according to the number of categories in the pseudo tag, setting the triggering probability of each sub-data enhancement method, and generating cascaded pixel random change; and finally, constructing a semi-supervised semantic segmentation model for the colorectal cancer pathological image false label. The problem that the semi-supervised semantic segmentation task of the colorectal cancer pathological image is low in precision is solved.

The invention belongs to the technical field of cerebral apoplexy diagnosis, and particularly relates to a cerebral apoplexy patient tongue picture automatic identification system and method based on multi-task learning, and the method comprises the steps: carrying out the multi-label classification of a tongue picture, dividing the tongue picture into a training set and a test set, and carrying out the data enhancement; constructing an LGAN model by using the tongue picture features, extracting picture features, capturing correlation between the features, and generating a classification result of each label; and optimizing model parameters by calculating a loss value between a prediction score vector and a real label, and evaluating a model performance index. According to the method, the problems that in the prior art, standardization and objectification of tongue picture recognition of a stroke patient are insufficient, diagnosis is inconsistent and accuracy is poor are solved, and by combining multi-task learning and an attention mechanism, accuracy and robustness of feature extraction are improved, dependence on doctor experience is reduced, and standardization and automation of tongue picture diagnosis are achieved.

The invention belongs to the technical field of information, and relates to a time sequence knowledge graph reasoning method and system for distinguishing similar historical structures. The method comprises the steps that relation reasoning paths between entity pairs are extracted, the confidence degree of each relation reasoning path is evaluated, the confidence degrees of the relation reasoning paths under different timestamps are aggregated, and relation reasoning scores are obtained; and then calculating an entity sub-graph structure score, obtaining a prediction score of the candidate entity according to the relationship reasoning score and the entity sub-graph structure score, and complementing the missing entity in the prediction query by using the candidate entity with the highest prediction score to realize time sequence knowledge graph reasoning. According to the method, similar historical structures are distinguished from the combination of the relation and the entity, modeling is carried out on the potential logic of the relation in the time sequence and the time evolution of the sub-graph structure of the entity, and candidate objects with similar sub-graph structures or similar relation connection can be distinguished; and the performance of the external reasoning task of the time sequence knowledge graph is effectively improved.

The invention relates to a small molecule virtual screening method and system based on a graph convolutional neural network, and the method comprises the steps: firstly obtaining the data of an active compound and a bait compound of a target spot, carrying out the preprocessing of the obtained data, constructing a graph convolutional neural network model, carrying out the training and evaluation of the model through a preprocessed data set, and obtaining a small molecule virtual screening result. And then scoring the small molecule compound data obtained in the compound database by using the trained graph convolutional neural network model, retaining the small molecule compound with high prediction score, and finally obtaining the known drug molecule data of the target spot. And performing compound similarity search on the small molecule compounds with high prediction scores and known drug molecules to obtain the small molecule compounds with similarity with the known drug molecules. Compared with the prior art, the method has the advantages that virtual screening is utilized, the drug research and development process is accelerated, the experiment cost is reduced, compound similarity search is combined on the basis, the screening accuracy is further improved, and the screening result range is narrowed.

The invention discloses a doctor-patient communication risk assessment and early warning system and method based on an internet hospital, and the system comprises a basic feature calculation module which is used for calculating the basic feature score of the early warning according to the collected online inquiry data of a doctor and a patient, and calculating the weight ratio; the dynamic feature analysis module is used for taking the dynamic feature factors as input of the constructed patient end prediction model and the constructed doctor end prediction model and respectively calculating to obtain patient dynamic prediction scores and doctor dynamic prediction scores; the risk fusion prediction module is used for performing feature fusion and final risk prediction by taking the patient and doctor dynamic prediction scores, the number of times of previous complaints of the patient, the number of times of previous complaints of the doctor and the basic feature scores as input of a constructed fusion prediction model, and performing calculation to obtain a doctor-patient communication risk score; and the early warning triggering module is used for triggering an intelligent early warning mechanism when the doctor-patient communication risk score exceeds a set threshold value. According to the invention, efficient and accurate doctor-patient communication risk assessment and intelligent early warning can be realized.

The invention discloses a two-stage few-sample automatic fact checking method, electronic equipment and a storage medium, and belongs to the technical field of information processing. In order to improve the accuracy and efficiency of information verification, the method comprises the following steps: selecting a public fact verification data set to construct a test set; constructing an evidence retrieval model, and retrieving the documents of the test set by adopting the evidence retrieval model to obtain candidate documents; constructing a sentence selection model, inputting each sentence in the candidate document and the statement into a prompt template to be combined into the input of the sentence selection model, and outputting the correlation score of the sentences as an evidence sentence for statement verification; constructing a pre-training language model and finely adjusting the pre-training language model; and inputting an evidence sentence for statement verification and a statement to be verified into a prompt template to be combined into input of a pre-training language model, inputting the input into the pre-training language model after fine tuning to obtain natural language output, then mapping the natural language output to a classification label, and calculating a prediction score according to the generation probability of an output sequence to obtain a final prediction result.

Computer program products, computer systems, and computer-implemented methods for making and using computational models for prediction of molecular recognition (MR) between a nanomaterial (NM) MR binder and an analyte. Methods for making a computational model involve selecting a candidate NM MR binder and conducting a physical test to determine whether MR occurs between the candidate NM MR binder and an analyte, and correlating features of the candidate NM MR binder with an experimental result obtained from the physical test to produce predictive information for the computational model. Methods for using the computational model involve receiving features of an untested candidate NM MR binder and analyzing the features to produce a prediction score that represents an expected experimental result of a physical test of the untested candidate MR binder and associating the prediction score with the untested candidate MR binder.