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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract: A wakesurfing boat for creating a wave suitable for surfing in the wake of the boat. A length to beam (L/B) ratio of 3.21 combined with a rounded hull bottom proximate the transom minimizes losses in the wake wave. Elliptical arcuate portions extending from starboard and port points on the transom to selected positions on the respective starboard and port bulwarks further minimize losses in the wake wave. The hull bottom defines an M-shaped portion and a V-shaped portion. A propulsion system includes an exhaust pipe projecting from the hull bottom below the waterline, with an exhaust opening forward of the transom. A propeller has a hub and at least three blades. A blade area, defined by a sum of the area of the blades, is larger than 70% of a disk area, defined by a sum of the hub and the blades.

Abstract: A wakesurfing boat for creating a wave suitable for surfing in the wake of the boat. A length to beam (L/B) ratio of 3.21 combined with a rounded hull bottom proximate the transom minimizes losses in the wake wave. Elliptical arcuate portions extending from starboard and port points on the transom to selected positions on the respective starboard and port bulwarks further minimize losses in the wake wave. The hull bottom defines an M-shaped portion and a V-shaped portion. A propulsion system includes an exhaust pipe projecting from the hull bottom below the waterline, with an exhaust opening forward of the transom. A propeller has a hub and at least three blades. A blade area, defined by a sum of the area of the blades, is larger than 70% of a disc area, defined by a sum of the hub and the blades.

Abstract: A wakesurfing boat and a hull for a wakesurfing boat. The hull includes a bottom having a preselected length extending from a bow to a stern. The bottom includes a central ridge and port and starboard ridges extending below respective port and starboard bulwarks to define a substantially M-shaped bottom with concave portions to port and starboard of the central ridge. Port and starboard sponsons extend below the port and starboard bulwarks proximate the stern. A trim wedge projects below the stern. Two rounded stern portions are provided proximate the stern. In a dynamic surfing mode, with selectively-fillable ballast tanks partially or fully flooded, water flowing through the port and starboard concave portions, combined with the ballast, increases resistance to the hull, resulting in a larger wake wave. Water passing around the structural features proximate the stern is directed toward a convergent zone in the wake, resulting in an improved wake wave shape.

Abstract: A wakesurfing boat and a hull for a wakesurfing boat. The hull includes a bottom having a preselected length extending from a bow to a stern. The bottom includes a central ridge and port and starboard ridges extending below respective port and starboard bulwarks to define a substantially M-shaped bottom with concave portions to port and starboard of the central ridge. Port and starboard sponsons extend below the port and starboard bulwarks proximate the stern. A trim wedge projects below the stern. Two rounded stern portions are provided proximate the stern. In a dynamic surfing mode, with selectively-fillable ballast tanks partially or fully flooded, water flowing through the port and starboard concave portions, combined with the ballast, increases resistance to the hull, resulting in a larger wake wave. Water passing around the structural features proximate the stern is directed toward a convergent zone in the wake, resulting in an improved wake wave shape.

Abstract: A wakesurfing boat and a hull for a wakesurfing boat. The hull includes a bottom having a preselected length extending from a bow to a stern. The bottom includes a central ridge and port and starboard ridges extending below respective port and starboard bulwarks to define a substantially M-shaped bottom with concave portions to port and starboard of the central ridge. Port and starboard sponsoons extend below the port and starboard bulwarks proximate the stern. A trim wedge projects below the stern. Two rounded stern portions are provided proximate the stern. In a dynamic surfing mode, with selectively-fillable ballast tanks partially or fully flooded, water flowing through the port and starboard concave portions, combined with the ballast, increases resistance to the hull, resulting in a larger wake wave. Water passing around the structural features proximate the stern is directed toward a convergent zone in the wake, resulting in an improved wake wave shape.

Abstract: A wakesurfing boat includes a hull. A bottom of the hull defines a W-shape in cross-section, the W-shape including a central inverted V-shaped portion. A ballast system includes a plurality of sets of ballast tanks. The boat has a plurality of modes: a cruising mode with ballast tanks empty, a first portion of the bottom in contact with the water, a stern-down trim ?1; a ballasted static mode with ballast tanks filled with ballast water, the hull displacing more water, a larger portion of the bottom in contact with the water, a bow-down trim ?2; and a dynamic surfing mode with ballast tanks filled with ballast water, an intermediate portion of the bottom in contact with the water, and an intermediate trim ?3. A wake wave, generated in the dynamic surfing mode is larger than a wake wave generated in the cruising mode.