The present disclosure relates to a method and device for designing a target-specific drug in which a deep learning algorithm is combined with a water pharmacophore model. More particularly, the present disclosure relates to a device and method for generating the library of novel compounds by securing specificity to a target protein through a water pharmacophore (WP) model, and then performing deep learning.

The present disclosure relates to a method and device for designing a target-specific drug in which a deep learning algorithm is combined with a water pharmacophore model. More particularly, the present disclosure relates to a device and method for generating the library of novel compounds by securing specificity to a target protein through a water pharmacophore (WP) model, and then performing deep learning.

A drug library management system facilitates centralized management of the drug libraries that are used by various infusion pumps, including in clinical environments that have different types and/or versions of infusion pumps. Medications, administration rules, critical care area rules, and the like can be maintained using the drug library management system. The drug library management system can generate and distribute drug library data in pump-specific formats or other customized formats as needed.

A drug library management system facilitates centralized management of the drug libraries that are used by various infusion pumps, including in clinical environments that have different types and/or versions of infusion pumps. Medications, administration rules, critical care area rules, and the like can be maintained using the drug library management system. The drug library management system can generate and distribute drug library data in pump-specific formats or other customized formats as needed.

Aspects of the present disclosure include methods for identifying a set of fluorophore-biomolecule reagent pairs for characterizing a sample by flow cytometry. Methods according to certain embodiments include calculating a spectral spillover spreading parameter for a plurality of fluorophores, pairing each fluorophore with a biomolecule that is specific for a biomarker of a cell in the sample to generate a plurality of fluorophore-biomolecule reagent pairs, generating an adjusted spillover spreading matrix for the fluorophore-biomolecule reagent pairs based on the spectral spillover spreading parameter of each fluorophore and a biomarker classification parameter and identifying an optimal set of fluorophore-biomolecule reagent pairs based on the calculated spillover spreading values from the adjusted spillover spreading matrix. Systems and non-transitory computer readable storage medium for practicing the subject methods are also provided.

Aspects of the present disclosure include methods for identifying a set of fluorophore-biomolecule reagent pairs for characterizing a sample by flow cytometry. Methods according to certain embodiments include calculating a spectral spillover spreading parameter for a plurality of fluorophores, pairing each fluorophore with a biomolecule that is specific for a biomarker of a cell in the sample to generate a plurality of fluorophore-biomolecule reagent pairs, generating an adjusted spillover spreading matrix for the fluorophore-biomolecule reagent pairs based on the spectral spillover spreading parameter of each fluorophore and a biomarker classification parameter and identifying an optimal set of fluorophore-biomolecule reagent pairs based on the calculated spillover spreading values from the adjusted spillover spreading matrix. Systems and non-transitory computer readable storage medium for practicing the subject methods are also provided.

Hydrogen being a clean, highly abundant and renewable fuel, is a promising alternative for conventional energy sources. Mostly, this hydrogen is stored in the form of hydrides. The existing methods for identification of material for hydrogen storage as expensive and time consuming. A method and system of identification of materials for hydrogen storage has been provided. The method provides a machine learning technique to predict the hydrogen storage capacity of materials, using only the compositional information of the compound. A random forest model used in the work was able to predict the gravimetric hydrogen storage capacities of intermetallic compounds. The method and system is also configured to predict the thermodynamic stability of the intermetallic compound.

A drug-screening system includes an encoding module, a candidate-drug generating module and a drug-ranking module. The encoding module is configured to encode a drug expression and at least one drug-ranking indicator to generate a first encoding variable. The candidate-drug generating module is configured to train a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The drug-ranking module is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs.

An apparatus for constructing a library for deriving a material composition using empirical result, which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction.

An apparatus for constructing a library for deriving a material composition using empirical result, which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction.