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    MACHINE LEARNING DRUG EVALUATION USING LIQUID CHROMATOGRAPHIC TESTING
    14.
    发明公开
    MACHINE LEARNING DRUG EVALUATION USING LIQUID CHROMATOGRAPHIC TESTING 审中-公开

    公开(公告)号：US20240021277A1

    公开(公告)日：2024-01-18

    申请号：US17864393

    申请日：2022-07-14

    Applicant: William Scott HOPKINS ,  Centre for Eye and Vision Research Limited ,  The Hong Kong Polytechnic University

    Inventor： Myo Win ZAW ,  William Scott HOPKINS ,  Ming Yan, Allen CHEONG

    IPC: G16C20/50 ,  G16C20/70

    CPC classification number: G16C20/50 ,  G16C20/70

    Abstract: A machine learning system predicts a physicochemical property (e.g., lipophilicity) of candidate small molecules for pharmaceuticals. A machine learning model is constructed that is trained from a database of small molecule physicochemical properties including known lipophilicity and known retention time in a liquid chromatography column to create a learned association between lipophilicity and liquid chromatography retention time. A candidate small molecule having unknown lipophilicity and unknown retention time is applied to a liquid chromatography column. The retention time of the candidate small molecule in the liquid chromatography column is measured. The measured retention time in the liquid chromatography column is applied to the machine learning model to obtain lipophilicity for the candidate small molecule. One or more candidate small molecules having a lipophilicity value from approximately 1 to approximately 3 are selected from the machine learning model. The identified candidate small molecules are tested for pharmaceutical activity.

    Method of classifying conformers
    15.
    发明授权
    Method of classifying conformers 有权

    公开(公告)号：US11862295B1

    公开(公告)日：2024-01-02

    申请号：US17983075

    申请日：2022-11-08

    Applicant: UNITED ARAB EMIRATES UNIVERSTIY

    Inventor： Alya A. Arabi

    IPC: G16B15/30 ,  G16C20/50 ,  G16C10/00

    CPC classification number: G16B15/30 ,  G16C10/00 ,  G16C20/50

    Abstract: A system and method for classifying conformers of a molecule are provided. The methods for classifying conformers of a molecule include selecting a target molecule, generating a list of conformers of the target molecule, completing a quantum mechanics (QM) simulation for each conformer, extracting an electronic energy for each conformer from the corresponding QM simulation, calculating average electron density (AED) values corresponding to a most electronegative group of the target molecule, generating a plot of the electronic energies vs. the calculated AED values, and classifying conformers based on this plot. Similar methods can also be used to predict shapes of electrostatic potential (ESP) maps for conformers of a molecule. These ESP maps can, in turn, be used to identify conformers of the molecule having desired chemical or pharmaceutical properties.

    TRANSFORMER-BASED GRAPH NEURAL NETWORK TRAINED WITH STRUCTURAL INFORMATION ENCODING
    16.
    发明公开
    TRANSFORMER-BASED GRAPH NEURAL NETWORK TRAINED WITH STRUCTURAL INFORMATION ENCODING 审中-公开

    公开(公告)号：US20230402136A1

    公开(公告)日：2023-12-14

    申请号：US17806075

    申请日：2022-06-08

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Shuxin ZHENG ,  Yu SHI ,  Tie-Yan LIU

    IPC: G16C20/70 ,  G16C60/00 ,  G16C20/50 ,  G06N3/04 ,  G06N5/04

    CPC classification number: G16C20/70 ,  G16C60/00 ,  G16C20/50 ,  G06N3/04 ,  G06N5/04

    Abstract: A computing system is provided, including a processor configured to, during a training phase, provide a training data set, including a pre-transformation molecular graph and post-transformation energy parameter value representing an energy change in a molecular system following an energy transformation. The pre-transformation graph includes a plurality of normal nodes connected by edges representing a distance and a bond between a pair of the normal nodes. The processor is further configured to encode structural information in each pre-transformation molecular graph as learnable embeddings, the structural information describing the relative positions of the atoms represented by the normal nodes. The structural information includes an edge encoding representing a type of bond between a pair of normal nodes in each pre-transformation molecular graph, and a spatial encoding representing a shortest path distance along the edges between a pair of normal nodes in each pre-transformation molecular graph.

    System and method for evaluation of at least one potential tastant from a plurality of tastants
    19.
    发明授权
    System and method for evaluation of at least one potential tastant from a plurality of tastants 有权

    公开(公告)号：US11783920B2

    公开(公告)日：2023-10-10

    申请号：US16783824

    申请日：2020-02-06

    Applicant: Tata Consultancy Services Limited

    Inventor： Anukrati Goel ,  Kishore Gajula ,  Rakesh Gupta ,  Beena Rai

    IPC: G16C20/10 ,  G16C20/30 ,  G16C20/64 ,  G16C20/50

    CPC classification number: G16C20/10 ,  G16C20/30 ,  G16C20/50 ,  G16C20/64

    Abstract: A processor implemented method of evaluating at least one potential tastant from a plurality of tastants is provided. The processor implemented method includes at least one of: receiving, information associated with a plurality of molecular activities; generating, a plurality of data-based models based on the known taste index associated with at least one tastant and information from associated molecular structure/descriptors; classifying, a new molecule based on the generated data-based models for the at least one tastant; screening, the one or more classified new molecules in an applicability domain of the generated data-based models based on the physics-based models by at least one molecular modeling technique; and evaluating, the at least one potential tastant from the screened molecules based on at least one of a bioavailability and a toxicity. In an embodiment, the plurality of molecular activities corresponds to a taste index and a binding energy.

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