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公开(公告)号：US20220009966A1
公开(公告)日：2022-01-13
申请号：US17487225
申请日：2021-09-28
发明人： Payel Das , Flaviu Cipcigan , James L. Hedrick , Yi Yan Yang , Kahini Wadhawan , Inkit Padhi , Enara C Vijil , Pang Kern Jeremy Tan
IPC分类号： C07K7/08
摘要： De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.
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公开(公告)号：US20210363183A1
公开(公告)日：2021-11-25
申请号：US16880280
申请日：2020-05-21
发明人： Payel Das , Flaviu Cipcigan , James L. Hedrick , Yi Yan Yang , Kahini Wadhawan , Inkit Padhi , Enara C Vijil , Pang Kern Jeremy Tan
IPC分类号： C07K7/08
摘要： De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.
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公开(公告)号：US11174289B1
公开(公告)日：2021-11-16
申请号：US16880280
申请日：2020-05-21
发明人： Payel Das , Flaviu Cipcigan , James L. Hedrick , Yi Yan Yang , Kahini Wadhawan , Inkit Padhi , Enara C Vijil , Pang Kern Jeremy Tan
摘要： De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.
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公开(公告)号：US20210110255A1
公开(公告)日：2021-04-15
申请号：US16653737
申请日：2019-10-15
发明人： Payel Das , Tom D. J. Sercu , Kahini Wadhawan , Cicero Nogueira Dos Santos , Inkit Padhi , Sebastian Gehrmann
摘要： A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.
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公开(公告)号：US11481626B2
公开(公告)日：2022-10-25
申请号：US16653737
申请日：2019-10-15
发明人： Payel Das , Tom D. J. Sercu , Kahini Wadhawan , Cicero Nogueira Dos Santos , Inkit Padhi , Sebastian Gehrmann
摘要： A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.
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公开(公告)号：US20220076137A1
公开(公告)日：2022-03-10
申请号：US17016640
申请日：2020-09-10
IPC分类号： G06N3/12 , G06F16/245 , G06N20/00
摘要： A query-based generic end-to-end molecular optimization (“QMO”) system framework, method and computer program product for optimizing molecules, such as for accelerating drug discovery. The QMO framework decouples representation learning and guided search and applies to any plug-in encoder-decoder with continuous latent representations. QMO framework directly incorporates evaluations based on chemical modeling, analysis packages, and pre-trained machine-learned prediction models for efficient molecule optimization using a query-based guided search method based on zeroth order optimization. The QMO features efficient guided search with molecular property evaluations and constraints obtained using the predictive models and chemical modeling and analysis packages. QMO tasks include optimizing drug-likeness and penalized log P scores with similarity constraints and improving the target binding affinity of existing drugs to pathogens such as the SARS-CoV-2 main protease protein while preserving the desired drug properties. QMO tasks further improves optimizing antimicrobial peptides toward lower toxicity.
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公开(公告)号：US20210366580A1
公开(公告)日：2021-11-25
申请号：US16880021
申请日：2020-05-21
发明人： Payel Das , Flaviu Cipcigan , Kahini Wadhawan , Inkit Padhi , Enara C Vijil , Pin-Yu Chen , Aleksandra Mojsilovic , Tom D.J. Sercu , Cicero Nogueira dos Santos
摘要： Techniques for filtering artificial intelligence (AI)-designed molecules for laboratory testing provided. According to an embodiment, computer implemented method can comprise selecting, by a system operatively coupled to a processor, a first subset of AI-designed molecules from a set of AI-designed molecules as candidate pharmaceutical agents based on classification of the AI-designed molecules using one or more classifiers. The method further comprises selecting, by the system, a second subset of the candidate pharmaceutical agents for wet laboratory testing based on evaluation of molecular interactions between the candidate pharmaceutical agents and one or more biological targets using one or more computer simulations.
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