Abrégé

Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
• G16B 40/20 - Analyse de données supervisée

Abrégé

Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
• G16B 40/20 - Analyse de données supervisée

Abrégé

Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
• G16B 40/20 - Analyse de données supervisée

Classes IPC  ?

• G16B 15/20 - Repliement de protéines ou de domaines
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 35/10 - Conception de bibliothèques
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs

Abrégé

Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Classes IPC  ?

• G16B 15/20 - Repliement de protéines ou de domaines
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 35/10 - Conception de bibliothèques
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs

Abrégé

Presented herein are systems and methods for predicting which amino acid sites of a target proteins of interest will be binding sites - for example, locations and/or identifications of particular amino acid sites - that are amenable or likely to participate in binding interactions with other ligands, such as other proteins. These binding site predictions may, for example, be generated for target proteins that are implicated in disease and, accordingly, be targets for potential new biologic drugs. Binding site prediction technologies described herein may thus be used to guide design and/or testing of new and/or custom biologic drugs, either experimentally or in-silico. In this manner, binding site prediction technologies of the present disclosure can facilitate design and/or testing of new biologic drugs, leading to new and improved candidates and/or improving, among other things, developmental efficiency, success rates of clinical trials, and time to market.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 40/20 - Analyse de données supervisée

Abrégé

Presented herein are systems and methods for predicting which amino acid sites of a target proteins of interest will be binding sites—for example, locations and/or identifications of particular amino acid sites—that are amenable or likely to participate in binding interactions with other ligands, such as other proteins. These binding site predictions may, for example, be generated for target proteins that are implicated in disease and, accordingly, be targets for potential new biologic drugs. Binding site prediction technologies described herein may thus be used to guide design and/or testing of new and/or custom biologic drugs, either experimentally or in-silico. In this manner, binding site prediction technologies of the present disclosure can facilitate design and/or testing of new biologic drugs, leading to new and improved candidates and/or improving, among other things, developmental efficiency, success rates of clinical trials, and time to market.

Classes IPC  ?

• G01N 33/48 - Matériau biologique, p. ex. sang, urineHémocytomètres
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
• G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux

Abrégé

Presented herein are systems and methods for prediction of protein interfaces for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule, such as another protein or peptide. In certain embodiments, graph-based neural network models described herein may receive, as input, a representation (e.g., a graph representation) of a complex comprising a target and a partially-defined custom biologic. Portions of the partially-defined custom biologic may be known, while other portions, such an amino acid sequence and/or particular amino acid types at certain locations of an interface, are unknown and/or to be customized for binding to a particular target. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
• G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux

Abrégé

Presented herein are systems and methods for prediction of protein sequences, such as interfaces and/or other portions of custom biologics, e.g., for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures of a custom biologic (e.g., a protein and/or peptide) that is being designed.

Classes IPC  ?

• G16B 35/10 - Conception de bibliothèques
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 40/20 - Analyse de données supervisée
• G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

Presented herein are systems and methods for prediction of protein interfaces for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule, such as another protein or peptide. In certain embodiments, graph-based neural network models described herein may receive, as input, a representation (e.g., a graph representation) of a complex comprising a target and a partially-defined custom biologic. Portions of the partially-defined custom biologic may be known, while other portions, such an amino acid sequence and/or particular amino acid types at certain locations of an interface, are unknown and/or to be customized for binding to a particular target. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs

Abrégé

Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.

Classes IPC  ?

• G16B 15/20 - Repliement de protéines ou de domaines
• G16B 35/00 - TIC spécialement adaptées aux bibliothèques combinatoires in silico d’acides nucléiques, de protéines ou de peptides
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire

Abrégé

in silico in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (Al) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. Al-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. Furthermore, in certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire

Abrégé

Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (Al) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. Al-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. Furthermore, in certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule.

Classes IPC  ?

• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire

Abrégé

Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.

Classes IPC  ?

• G16B 15/20 - Repliement de protéines ou de domaines
• G16B 35/00 - TIC spécialement adaptées aux bibliothèques combinatoires in silico d’acides nucléiques, de protéines ou de peptides
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 40/20 - Analyse de données supervisée

Abrégé

Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.

Classes IPC  ?

• G16B 15/20 - Repliement de protéines ou de domaines
• G16B 35/00 - TIC spécialement adaptées aux bibliothèques combinatoires in silico d’acides nucléiques, de protéines ou de peptides
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire