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公开(公告)号：US20240274244A1
公开(公告)日：2024-08-15
申请号：US18441606
申请日：2024-02-14
发明人： Yui Tik Pang , James C. Gumbart , Katie Kuo
摘要： Systems and methods are disclosed for generating macromolecule conformations using deep learning and for training deep learning networks to generate macromolecular conformations. The system can, for example, generate the transition between two end states based on energy evaluation. Additionally, the system can output all-atom structures of the end states in different conformations. These macromolecules include proteins, RNA, and polymers, among others. The present disclosure also includes methods for training a deep-learning network to model macromolecular conformations according to a specific objective function. The training can include employing a generative adversarial neural network, for example, by using molecular dynamics or energy simulations to validate predicted states.
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2.发明公开公开(公告)号：US20240185964A1
公开(公告)日：2024-06-06
申请号：US18550047
申请日：2022-03-02
申请人： UVUE LIMITED
发明人： Attila Bagoly , David Galindo , Jia Liu , Jonathan Ward
摘要： Disclosed is a system for processing molecular information and a method of facilitating inter-party communication relating to molecular fingerprints. The system comprises a server arrangement configured to receive an input of the molecular information, wherein the molecular information comprises information pertaining to molecular structure of at least one molecule: process the molecular information to map the molecular structure of each of the at least one molecule in the input to a molecular fingerprint corresponding thereto using neural networks. wherein the molecular fingerprint is a representation of the at least one molecule in a multi-dimensional space that enables comparison of the at least one molecule with other molecules: encrypt the molecular fingerprints using a symmetric encryption algorithm; and store the encrypted molecular fingerprints in a data repository.
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公开(公告)号：US20240177808A1
公开(公告)日：2024-05-30
申请号：US17990703
申请日：2022-11-20
发明人： ALYA A. ARABI , ALAA M. A. OSMAN
摘要： A system and method for identifying bioisosteres of a molecule are provided. These methods are particularly useful for confirming that amides and 1,2,3-triazoles are bioisosteres of one another. The methods for evaluating bioisosteres of a molecule include selecting a first molecule of interest having an amide group as a first bioisostere, replacing the amide group with a 1,2,3-triazole group as a second bioisostere to obtain a second molecule, completing a quantum mechanics (QM) simulation for each molecule, calculating average electron density (AED) values corresponding to the first and second bioisosteres in the first and second molecules, respectively, and confirming the bioisosterism based on the calculated AED values of the biosiosteres. These methods can be further used to identify further bioisosteres thereof. present methods and systems can be used to aid in many applications including but not limited to the development of drug design.
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4.发明公开公开(公告)号：US20240170108A1
公开(公告)日：2024-05-23
申请号：US18490025
申请日：2023-10-19
发明人： Broto CHAKRABARTY , Siladitya PADHI , Riya Dilipbhai SADRANI , Rajgopal SRINIVASAN , Arijit ROY
摘要： Traditional drug discovery methods are target-based, time- and resource-intensive, and require a lot of resources for the initial hit molecule identification. Phenotype-based drug screening requires differential gene expression data of a large number of molecules for different combinations of cell-line, time point and dosage. Experimentally obtaining gene expression data for all these combinations is again a heavily resource-intensive process. The technical challenge in conventional methods that use prediction models is that they depend largely on the data processing and representation. The disclosure herein generally relates to drug-like molecule screening, and, more particularly, to a method and system for gene expression and machine learning-based drug screening. The embodiment, thus, provides a mechanism of a small molecule-induced gene expression prediction based on machine learning models. Moreover, the embodiments herein further provide a mechanism of screening of drug-like molecules using the machine learning model(s).
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5.发明公开公开(公告)号：US20240145041A1
公开(公告)日：2024-05-02
申请号：US18497139
申请日：2023-10-30
发明人： Hok Hei Tam , Varun Shivashankar , Nathan Sanders , Terran Lane , David Kolesky , Mostafa Karimi
摘要： The computer system applies machine learning techniques to train a computational model using data representing researched items and their known properties. The computer system applies the trained computational model to data representing the potential candidate items to predict whether such items have such properties. The trained computational model outputs one or more predictions about whether the potential candidate items are likely to have a property from among the plurality of types of properties that the computational model is trained to predict. The computer system allows multiple machine learning experiments to be defined, and then allows predictions from those multiple machine learning experiments to be queried, including accessing aggregate statistics for those predictions. In some implementations, a machine learning experiment can specify a computational model that is an ensemble of multiple models.
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公开(公告)号：US11942192B2
公开(公告)日：2024-03-26
申请号：US16927533
申请日：2020-07-13
摘要： Techniques facilitating density-functional theory determinations using a quantum computing system are provided. A system can comprise a first computing processor and a second computing processor. The first computing processor can generate a density-functional theory determination. The second computing processor can input a quantum density into the density-functional theory determination. The first computing processor can be operatively coupled to the second computing processor. Further, the first computing processor can be a classical computer and the second computing processor can be a quantum computer.
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7.发明公开公开(公告)号：US20240055071A1
公开(公告)日：2024-02-15
申请号：US18494372
申请日：2023-10-25
发明人： Xujun ZHANG , Benben LIAO , Shengyu ZHANG , Tingjun HOU
摘要： An artificial intelligence-based compound processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product relates to an artificial intelligence technology. The method includes obtaining an active compound for a target protein; performing compound generation processing on an attribute property of the active compound to obtain a first candidate compound; performing molecular docking processing on the active compound and the target protein to obtain molecular docking information respectively corresponding to a plurality of molecular conformations of the active compound; screening the plurality of molecular conformations based on the molecular docking information respectively to identify a second candidate compound corresponding to the active compound; and constructing a compound library for the target protein based on the first candidate compound and the second candidate compound.
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公开(公告)号：US11900225B2
公开(公告)日：2024-02-13
申请号：US17000746
申请日：2020-08-24
发明人： Kenta Oono , Justin Clayton , Nobuyuki Ota
摘要： A computer system for generating information regarding chemical compound includes one or more memories and one or more processors configured to generate information regarding chemical compound based on a latent variable, and to evaluate the generated information regarding chemical compound based on desired characteristics, wherein generating the information regarding chemical compound is restricted by the desired characteristics.
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公开(公告)号：US20240021277A1
公开(公告)日：2024-01-18
申请号：US17864393
申请日：2022-07-14
申请人： William Scott HOPKINS , Centre for Eye and Vision Research Limited , The Hong Kong Polytechnic University
摘要： 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.
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公开(公告)号：US11862295B1
公开(公告)日：2024-01-02
申请号：US17983075
申请日：2022-11-08
发明人： Alya A. Arabi
摘要： 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.
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