Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G16B 40/20 - Supervised data analysis

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G16B 40/20 - Supervised data analysis

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G16B 40/20 - Supervised data analysis

IPC Classes  ?

• G16B 15/20 - Protein or domain folding
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 35/10 - Design of libraries
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

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.

IPC Classes  ?

• G16B 15/20 - Protein or domain folding
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 35/10 - Design of libraries
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 40/20 - Supervised data analysis

Abstract

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.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

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.

IPC Classes  ?

• G16B 35/10 - Design of libraries
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 40/20 - Supervised data analysis
• G06N 5/02 - Knowledge representationSymbolic representation
• G06N 3/08 - Learning methods

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

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.

IPC Classes  ?

• G16B 15/20 - Protein or domain folding
• G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction

Abstract

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.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction

Abstract

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.

IPC Classes  ?

• G16B 15/20 - Protein or domain folding
• G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 40/20 - Supervised data analysis

Abstract

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.

IPC Classes  ?

• G16B 15/20 - Protein or domain folding
• G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction