Predicting Drug Interactions and New Drug Discovery using AI

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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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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Using neural networks to accurately predict drug side effects when multiple drugs are administered concurrently. The technique involves training a multimodal cell complex neural network (MCXN) on a dataset containing drugs, proteins, interactions, and side effects. The MCXN can predict side effect probabilities, severities, and frequencies for combinations of 3 or more drugs not in the training set. It can also monitor changes in side effects over time due to factors like new drugs or lifestyle. This allows personalized treatment optimization and proactive side effect management.

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AI-based drug molecule screening method to efficiently find valuable drug candidates from large compound libraries. The method involves steps like preprocessing compounds, homology modeling, activity prediction, and molecular docking scoring using AI models. It leverages AI techniques like image encoding, text transformers, and neural networks to accurately represent molecules and proteins for screening. This allows efficient identification of target drug molecules for specific proteins by fusing their representations, predicting activity, and docking.

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Generating enhanced graphic user interfaces for selecting drug candidates. The GUI includes a set of sequences, each sequence contains a respective set of activities (e.g., biomedical activities, such as anti-microbial activity, immunomodulatory activity, receptor binding activity, self-aggregation activity, cell-penetrating activity, anti-viral activity, peptidergic activity, anti-cancer activity, anti-fungal activity, anti-prionic activity, etc.) pertaining to the application.

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Automated prediction of a clinical trial outcome that minimizes the distance between the ground truth molecule embedding and predicted one. The prediction is based on model parameters.

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Method for predicting drug interactions using structural information of drugs, as well as screening drug combinations with low probability of adverse interactions. The method involves calculating structural similarity profiles of drugs, inputting them into a trained model, predicting interactions using the model, and determining combinations with low interaction potential when no interaction is predicted. The interaction predictions are output as sentences describing the interaction mechanisms.

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An intelligent pharmacovigilance platform that uses artificial intelligence to improve accuracy and efficiency in monitoring adverse drug reactions. The platform receives input data, processes it using AI models to generate commands, checks user authority, performs tasks using AI models, visualizes results, and shares data. The AI models are selected based on user and task. The platform learns from user reviews and applies exception handling for serious events. It also enables data sharing between systems.

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Using artificial intelligence (AI) to efficiently discover novel drug compounds by leveraging protein dialects to generate sequences with desired activities. The AI model iteratively trains subsets of layers to identify sequences for each dialect's specific grammar. It generates shared portions of sequences that satisfy secondary objectives. Final layers receive primary objectives to determine remaining portions. Sharing subsets saves computation by avoiding recomputing shared portions for each dialect.

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Detecting the risk of torsades de pointes (TdP) arrhythmia and the underlying mechanism using artificial intelligence on electrocardiogram (ECG) signals. A machine learning algorithm is trained on ECG data with labeled TdP risk factors like drug intake and genetic mutations. The algorithm can then analyze new ECGs to predict TdP risk and the cause like drug effect or long QT syndrome type.

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A system to provide personalized drug interaction and efficacy information to patients, caregivers, and healthcare professionals. The system uses a database of drug side effects, interactions, and management strategies. It allows users to input their medication lists and provides customized information on potential side effects, warnings, and recommendations for optimizing therapy. The system leverages knowledge mining, AI, and user contributions to improve drug safety and outcomes for people with comorbidities.

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Artificial intelligence engine for generating candidate drugs using experimental validation and peptide drug optimization. The AI engine receives signals from proxy organisms after administering candidate drugs. It analyzes the signals to detect biomarkers indicating drug effectiveness. This allows preclinical validation of drug candidates before clinical trials. The AI engine uses neural networks for drug discovery, design, and testing in a larger design space than conventional methods. It enlarges the drug space using encoding techniques and reduces complexity with causal inference.

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Method for predicting drug interactions using structural information of drugs, allowing prediction of interaction mechanisms and identifying drug combinations with lower probability of adverse interactions. The method involves calculating structural similarity profiles for two drugs, inputting them into a trained model, predicting interactions using the profiles, and interpreting the output sentences to determine interaction mechanisms. When no interaction is predicted, it indicates lower probability of adverse interactions.

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Artificial intelligence system to aid medical professionals in treating heart failure patients. The system uses a combination of pharmaceutical-specific models and an expert system to determine personalized treatment plans for heart failure patients. The pharmaceutical-specific models predict patient responses to dosage changes of different heart failure medications. An expert system with rules determines the prioritized sequence to adjust medication dosages based on the predicted responses. The system inputs patient data, runs the models, applies the rules, and recommends an updated prescription.

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Artificial intelligence engine for generating candidate drugs using causal inference and machine learning techniques. The engine enlarges the design space for drug discovery by encoding drug information like structure, activity, and semantics. It uses machine learning models like GANs and RNNs to efficiently search the enlarged space. The engine also uses benchmarking and reinforcement learning to iteratively improve the drug generation process. The encoded drug representations are aggregated from databases and translated into the AI engine's format. The engine generates, tests, and refines candidate drugs based on the encoded representations.

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Predicting the properties of a new compound affecting the nervous system using artificial intelligence and neuronal activity data. The method involves training an AI model on known compounds by analyzing their effects on neural activity. When a new compound's neural activity is inputted, the model predicts its properties. The AI learns the relationship between neural activity and compound properties by analyzing burst patterns in raster plots of neural activity recorded with microelectrodes.

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Predicting interactions between drugs and foods using machine learning to identify potential drug-food interactions and assess their likelihood. The method involves calculating similarities between drugs and foods based on known interactions, then using those similarities along with drug-drug and food-food similarities to predict interaction probabilities between new drug-food pairs. This allows identifying potential interactions between drugs and foods that haven't been previously reported.

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