AI-Powered Drug Discovery and Interaction Prediction

Predicting drug efficacy and side effects using machine learning to integrate chemical, cellular, and clinical data for more accurate drug discovery. The method involves acquiring drug chemical structure, pharmacological properties, and cellular action data. A machine learning model estimates drug properties based on the chemical and pharmacological data. Then, retraining the model using the estimated properties predicts efficacy and side effects. This integrates chemical and cellular data to predict clinical outcomes.

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Method for predicting compounds with improved drug properties by analyzing structural differences between compounds. The method involves using an artificial intelligence model that takes two compounds as input instead of just one. The model predicts the difference in drug properties between the two compounds based on their structural differences. This allows finding compounds with improved properties by swapping certain moieties from a starting compound. The predicted property differences, reliability, and meeting desired ranges are used to prioritize and recommend alternative compounds.

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Predicting adverse drug interactions by recovering missing attribute data for drugs. The method involves constructing a recovery model to fill in missing attribute values for drugs based on shared and unique attributes. This recovered data is then used to train a prediction model for adverse drug interactions. By recovering missing attributes, the prediction accuracy improves compared to ignoring missing values. This enables more accurate adverse drug interaction prediction and safer medication.

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Method for predicting drug interaction effects using pre-trained molecular graph models to improve accuracy and scalability compared to traditional methods. The approach involves pre-training a multi-drug representation model using molecular graph structures and drug target information. This pre-trained model is then used to predict drug interactions and combinations by leveraging learned drug-drug and drug-internal latent feature representations. The pre-training allows the model to extract potential characteristics of drugs from multiple aspects and integrates potential characteristics of multiple drugs for scalable multi-drug molecular representation. It also enables the model to predict interactions using only structural and target data without complex feature engineering.

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Predicting the amount of pharmacokinetic change of one drug in a pair of drugs under the influence of the other drug. The prediction is based on machine learning models using drug property and interaction data. It involves receiving the two drugs as input, determining if they match interaction data, and using a learned prediction model to infer the change in pharmacokinetics of the first drug caused by the second drug. This allows predicting potential drug interactions and evaluating the impact of swapping one drug for another. The models leverage drug properties like classification, structure, and metabolism.

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A drug pathway prediction method based on network reasoning to predict potential drug-pathway relationships without relying on negative samples. The method involves constructing a heterogeneous network of substructures, drugs, and pathways from molecular and clinical data. It then uses network reasoning algorithms to calculate drug-pathway relationships by propagating initial resources through the network. This allows predicting potential pathways for new drugs and repurposing old drugs based on their substructure similarity. The method provides a drug pathway prediction model for finding new therapeutic applications of existing drugs by leveraging their similarity to known drugs with desired pathway effects.

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A drug-drug interaction prediction method based on tensor neural networks that improves interpretability and enables predicting multiple types of interactions between the same pair of drugs. The method decomposes drugs into substructures, constructs substructure interaction tensors, and uses a tensor neural network to learn the substructure interactions. This allows predicting drug-drug interactions by inferring the substructure interactions. The decomposed substructure representation provides interpretability compared to directly studying whole drug structures.

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Method for predicting the effect of combination drugs using a machine learning ensemble model to efficiently predict the effect of new drug combinations. The method involves converting gene-level cell and drug data into pathway-level data, learning the cell, drug, and correlation data using gradient boosting classifiers, and ensemble learning the classifiers to make combination drug effect predictions. This allows predicting drug synergy by inferring from cell, drug, and correlation data using machine learning.

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Predicting adverse drug reactions (ADRs) and identifying potential target proteins involved using only the molecular structure of a drug as input. The method involves calculating drug-target interaction features from the drug's structure and target protein structures, running classifier models to predict ADRs based on those features, and generating output indicating predicted ADRs. This allows automated prediction of ADRs for new drugs and identification of potential targets contributing to ADRs.

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Using deep learning models with Transformer networks to predict and explain drug metabolism interactions. The method involves learning drug molecular graph and substructure representations using Transformer networks, and then using joint attention mechanisms to capture the interaction between drugs. This allows predicting the type and magnitude of metabolic interactions between drugs while explaining the structural basis. The approach leverages deep learning to improve medication safety by accurately identifying and explaining drug metabolism interactions.

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Method for predicting drug-protein interactions using a convolutional neural network (CNN) that encodes drugs and proteins jointly to capture their consistent relationship. The method involves constructing a CNN model to calculate interaction probabilities between drugs and proteins based on their jointly encoded representations. This allows the model to analyze the potential identity between drugs and proteins, which can improve drug-protein interaction prediction compared to independent encoding methods. The CNN encodes drugs and proteins simultaneously to capture their consistent relationship, unlike previous methods that encoded drugs and proteins separately. This joint encoding helps the model analyze the potential identity between drugs and proteins, which can improve drug-protein interaction prediction compared to independent encoding methods. The method addresses limitations of previous network-based methods that independently encoded drugs and proteins.

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Drug screening method and system using machine learning to predict protein inhibition and in vivo activity of drugs, and then optimize drug structures to improve both properties. The method involves: 1) collecting drug molecules with target protein inhibition or in vivo activity, processing the structures, and predicting protein inhibition and disease treatment effectiveness using a multi-task deep learning model. 2) Using reinforcement learning to train drug structures, extract high scoring substructures for protein inhibition and disease treatment, merge them, and optimize the merged structure using a pre-trained encoder-decoder. This generates new drugs with both improved target protein inhibition and disease treatment effectiveness.

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A drug-drug interaction prediction model called SSF-plus I that combines sequence and substructure features of drugs to improve accuracy compared to models using just molecular graphs or sequences. The SSF-plus I model captures broader feature information by integrating topological and sequential drug representations. It uses graph neural networks to extract structural features from drug molecules, but also separately encodes drug sequences using transformer-like self-attention. The model combines these sequence and substructure representations for drug interaction prediction. This allows it to leverage both molecular structure and sequence similarity to better capture drug interaction patterns.

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Predicting drug-target interactions using only sequence information using a pre-trained natural language processing model. The prediction includes first inputting each sequence of a drug and target protein into a pre-trained natural language processing model and outputting an output embedding; second generating a vector by connecting some tokens among the output embeddings of the natural language processing model; third inputting the vector into an artificial neural network model and outputting the drug-target interaction probability.

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Method for identifying drug products using AI engines specialized for specific groups of drugs instead of a single large model. The method involves filtering drug product characteristics to categorize drugs into groups. AI engines are trained for each group. When a drug needs identification, its characteristics are used to select the matching engine for accurate NDC prediction or verification. This improves efficiency by avoiding retraining a large model with many drugs. It also allows smaller entities to train their own engines for the drugs they distribute. A consensus engine checks training consistency across sources to validate new drugs.

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Predicting efficacy of drugs for treating diseases using a machine learning model that leverages drug-drug interaction and drug-target interaction data. The model takes as input the chemical structure of a drug and its known interactions with other drugs and targets. It predicts the drug's efficacy for a specific disease based on these interactions. The model is trained using approved drugs and their disease indications. This allows repurposing of existing drugs for new diseases by predicting their efficacy based on known interactions.

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A reliable drug-target correlation prediction method using subjective logic to predict drug-target associations and provide reliability estimates. The method involves training a subjective logic model to predict the probability of activity or inactivity for a given drug-target pair. The model uses evidence from known associations to calculate the reliability of the prediction. This allows prioritizing and ranking drug-target pairs based on both predicted correlation and reliability. The reliability estimates can help reduce experimental costs by avoiding waste on unreliable predictions.

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Food ingredient and drug constituent interaction detector platform that can be used at the moment of Food/Meal decision making which aids in achieving the desired effect of a drug, avoids interactions and is personalized and updated according to the data points on health parameters, taste and nutritional requirements of the consumer. The platform includes the creation of data layers that are connected together by means of a hash and the aggregating of such data into blocks referenced by a hash value.

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Method and device for checking drug interaction that identifies high-risk drug combinations using machine learning on medical records. The method involves generating a drug combination set from medical records, calculating odds ratios for each combination, finding fractions for each drug, and outputting combinations with high odds ratios, high drug fractions, and low quotients. This indicates combinations where the first drug's odds are high, the second drug's odds are low, and the first drug fraction is greater. This suggests interaction between the drugs, not just high-risk individual drugs.

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Predicting drug-drug interactions (DDIs) using chemical structure. The prediction includes evaluating a structural similarity between new, unseen drugs of interest and baseline drugs, based on the first substance data element and the relevant baseline drug data element; selecting a first subset of the plurality of baseline drugs, based on the first substance data element and the relevant baseline drug data element; and predicting a DDI between the first substance of interest and the second substance of interest, based on the first substance data element, the second substance data element, and the DDI data structure.

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