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.

According to one embodiment of the present invention, provided are a feature quantity calculating method which enables calculation of a feature quantity accurately showing chemical properties of a target structure, a screening method which enables efficient screening of a pharmaceutical candidate compound using a feature quantity, and a compound creating method which enable efficient creation of a three-dimensional structure of a pharmaceutical candidate compound using a feature quantity. In one aspect of the present invention, the feature quantity calculating method is a method including a target structure designating step of designating a target structure formed of a plurality of unit structures having chemical properties, a three-dimensional structure acquiring step of acquiring a three-dimensional structure from the plurality of unit structures for the target structure, and a probe feature quantity calculating step of calculating a feature quantity showing a cross-sectional area of one or more kinds of probes for the target structure, in which the probe is a structure in which a plurality of points having a real electric charge and generating a van der Waals force are disposed to be separated from each other.

According to one embodiment of the present invention, provided are a feature quantity calculating method which enables calculation of a feature quantity accurately showing chemical properties of a target structure, a screening method which enables efficient screening of a pharmaceutical candidate compound using a feature quantity, and a compound creating method which enable efficient creation of a three-dimensional structure of a pharmaceutical candidate compound using a feature quantity. In one aspect of the present invention, the feature quantity calculating method is a method including a target structure designating step of designating a target structure formed of a plurality of unit structures having chemical properties, a three-dimensional structure acquiring step of acquiring a three-dimensional structure from the plurality of unit structures for the target structure, and a probe feature quantity calculating step of calculating a feature quantity showing a cross-sectional area of one or more kinds of probes for the target structure, in which the probe is a structure in which a plurality of points having a real electric charge and generating a van der Waals force are disposed to be separated from each other.

A molecular structure acquisition method, an electronic device and a storage medium, which relate to the field of artificial intelligence such as deep learning, are disclosed. The method may include: performing, for an initial seed, the following first processing: generating M molecular structures according to the seed, M being a positive integer greater than one; taking the M molecular structures as candidate molecular structures, and selecting some molecular structures from the candidate molecular structures as progeny molecular structures; and performing evolutionary learning on the progeny molecular structures, taking the progeny molecular structures after evolutionary learning as the seed, and repeating the first processing until convergence reaches an optimization objective, and when the convergence reaches the optimization objective, a newly selected molecular structure is taken as a desired molecular structure.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

Disclosed is a method for classifying monitoring results from an analytical sensor system (20), by allowing (100) a first set of analyte sample solutions to interact with a ligand (3) and acquiring (101) a set of response data, extracting (102) at least one interaction parameter from the response data, and for each analyte sample solution providing (103) a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies (104) each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3). The machine learning algorithm is trained (200) using a set of interaction parameters extracted from response data obtained from interactions between a second set of analyte sample solutions with at least one ligand (3), and at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3).

Disclosed is a method for classifying monitoring results from an analytical sensor system (20), by allowing (100) a first set of analyte sample solutions to interact with a ligand (3) and acquiring (101) a set of response data, extracting (102) at least one interaction parameter from the response data, and for each analyte sample solution providing (103) a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies (104) each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3). The machine learning algorithm is trained (200) using a set of interaction parameters extracted from response data obtained from interactions between a second set of analyte sample solutions with at least one ligand (3), and at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3).