Abstract: Optical measurement of a sample that includes a structure-of-interest (SOI) optically coupled to an unknown structure is optically measured by extracting from the resulting spectral signal the spectral variation that is correlated to key parameters associated with the SOI and removing the spectral variation from unknown structure that is irrelevant to the key parameters. An offline process is used to generate a physics-based model from a number of calibration measurements using reconstructed spectral signals after removing the spectral variation from the spectral signals that is irrelevant to the key parameters. A machine learning model may be additionally generated using at least the spectral variations correlated to key parameters associated with the SOI. In an in-line process, a sample is measured by filtering the spectral signals from a sample to remove spectral effects from the unknown structure and using the physics-based model or using the trained machine learning model.

Abstract: Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

Abstract: Disclosed is a method for predicting an elution order of compounds in a mixture. The method includes (a) building a quantitative structure-retention relationship (QSRR) model and (b) predicting a chromatographic elution order of the compounds in the mixture on the basis of the QSRR model using mathematical programming. The mathematical programming is a non-linear programming technique in which a predicted elution order of the compounds is used as a constraint or a multi-objective optimization (MOO) in which a retention time prediction error and an elution order prediction error are used as objective functions. With the use of the method of the present disclosure, it is possible to optimize separation of complex mixtures in reversed-phase chromatography by enabling identification of accurate positions of individual compounds that provides higher certainty in identifying a given compound, e.g., during an “omics” analysis (proteomics, metabolomics, etc.).

Abstract: The present invention relates to a method for reducing the complexity of bio-crudes. The method includes (a) obtaining experimental data of quantitative and qualitative analyses for the bio-crudes, (b) grouping compounds contained in the bio-crudes according to a predetermined basis based on the experimental data, (c) selecting representative compounds from among the compounds belonging to the same group, and (d) reconstituting the bio-crudes as a mixture of the representative compounds.

Abstract: Disclosed is a method for predicting an elution order of compounds in a mixture. The method includes (a) building a quantitative structure-retention relationship (QSRR) model and (b) predicting a chromatographic elution order of the compounds in the mixture on the basis of the QSRR model using mathematical programming. The mathematical programming is a non-linear programming technique in which a predicted elution order of the compounds is used as a constraint or a multi-objective optimization (MOO) in which a retention time prediction error and an elution order prediction error are used as objective functions. With the use of the method of the present disclosure, it is possible to optimize separation of complex mixtures in reversed-phase chromatography by enabling identification of accurate positions of individual compounds that provides higher certainty in identifying a given compound, e.g., during an “omics” analysis (proteomics, metabolomics, etc.).

Abstract: Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

Abstract: The present invention relates to a method for reducing the complexity of bio-crudes. The method includes (a) obtaining experimental data of quantitative and qualitative analyses for the bio-crudes, (b) grouping compounds contained in the bio-crudes according to a predetermined basis based on the experimental data, (c) selecting representative compounds from among the compounds belonging to the same group, and (d) reconstituting the bio-crudes as a mixture of the representative compounds. In the method for reducing the complexity of the bio-crudes for process simulation according to the present invention, the number of constituent compounds of the bio-crudes is minimized and the physical properties of the bio-crudes are accurately represented using only experimental data obtained through GC-MS, thereby enabling accurate and efficient process simulation of the bio-crudes.