A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

Systems, methods, and/or apparatuses may be operative to perform a dialysis process that includes a displacer infusion process. In one embodiment, a method for determining a displacer compound may include constructing a plurality of target protein quantitative structure-activity relationship (QSAR) models, one for each of the plurality of binding sites, analyzing a set of candidate compounds using the plurality of QSAR models to determine a set of at least one potential compound with an affinity for binding to each of the plurality of binding sites, and selecting at least one displacer compound from the set of at least one potential compound. Other embodiments are described.

Systems, methods, and/or apparatuses may be operative to perform a dialysis process that includes a displacer infusion process. In one embodiment, a method for determining a displacer compound may include constructing a plurality of target protein quantitative structure-activity relationship (QSAR) models, one for each of the plurality of binding sites, analyzing a set of candidate compounds using the plurality of QSAR models to determine a set of at least one potential compound with an affinity for binding to each of the plurality of binding sites, and selecting at least one displacer compound from the set of at least one potential compound. Other embodiments are described.

Aspects of the present disclosure include methods for optimizing pharmacological compound development and methods for optimizing one or more modifications of a compound. Aspects of the present disclosure further include methods for designing treatments for a disease, and methods for designing optimized candidate compounds to treat a disease that causes one or more disease effects. Aspects of the present disclosure further include computer-implemented methods for training a model for pharmacological compound design, and computer-implemented methods for optimizing chemical modification of pharmacological compounds.

Aspects of the present disclosure include methods for optimizing pharmacological compound development and methods for optimizing one or more modifications of a compound. Aspects of the present disclosure further include methods for designing treatments for a disease, and methods for designing optimized candidate compounds to treat a disease that causes one or more disease effects. Aspects of the present disclosure further include computer-implemented methods for training a model for pharmacological compound design, and computer-implemented methods for optimizing chemical modification of pharmacological compounds.

The present invention relates to a method for predicting a drug-drug interaction and a drug-food interaction by using structural information of a drug and, more particularly, to a method for predicting the mechanism of action and activity of a drug interaction through interaction prediction results expressed by a standardized sentence. When using a method for predicting a drug interaction according to the present invention, a drug interaction can be predicted quickly and accurately, and in particular, activity information of an unknown compound can also be predicted by expressing a prediction result by means of a sentence, and thus the method is very useful for developing a drug exhibiting desired activity without causing adverse effects.

The present invention relates to a method for predicting a drug-drug interaction and a drug-food interaction by using structural information of a drug and, more particularly, to a method for predicting the mechanism of action and activity of a drug interaction through interaction prediction results expressed by a standardized sentence. When using a method for predicting a drug interaction according to the present invention, a drug interaction can be predicted quickly and accurately, and in particular, activity information of an unknown compound can also be predicted by expressing a prediction result by means of a sentence, and thus the method is very useful for developing a drug exhibiting desired activity without causing adverse effects.

Provided is a method and apparatus for determining a molecular conformation. The method of determining a molecular conformation includes generating candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model that is based on an artificial neural network (ANN), comparing energy values between the candidate conformations by inputting the candidate conformations to an ANN-based conformation selecting model, and determining a final conformation based on a result of the comparing.

Provided is a method and apparatus for determining a molecular conformation. The method of determining a molecular conformation includes generating candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model that is based on an artificial neural network (ANN), comparing energy values between the candidate conformations by inputting the candidate conformations to an ANN-based conformation selecting model, and determining a final conformation based on a result of the comparing.

A method and a system for predicting a molecular weight of a hydrocarbon fluid are provided. An exemplary method includes measuring a density of the hydrocarbon fluid, obtaining an alternative measurement of a physical property of the hydrocarbon fluid, calculating an index value for the hydrocarbon fluid from the alternative measurement, and calculating a predicted molecular weight using an equation that combines the density with the index value. The predicted molecular weight is provided as an output.