Abstract: A TLR9 inhibitor includes a compound of general formula (I): wherein the meanings of the variables are explained in the specification, or a stereoisomeric form or a mixture of stereoisomeric forms, or pharmaceutically acceptable salts thereof. A pharmaceutical composition can include compounds of the invention, which can be used in a method for inhibiting TLR9 activity in vitro or in vivo. The method can be performed by administering the compound to a subject to inhibit TLR9 activity, which can be used to treat a disease or disorder associated with TLR9.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: A method of inhibiting TNIK and/or MAP4K4 kinase can include: contacting the kinase with a compound of Formula A, wherein: ring 1 is an aromatic ring with or without hetero atoms; ring 2 is a hetero aromatic ring; ring 3 includes at least one hetero aromatic ring and optionally at least one cycloaliphatic ring fused with the at least one hetero aromatic ring; ring 4 is an aromatic ring with or without hetero atoms; Y is a bond or a linker; Y1 is a linker; each n is independently 0, 1, or 2; each o is independently 0. 1, 2, 3, 4, or 5; each R1, R6, R11, and R12 is independently a substituent; and RA is a ring structure, straight aliphatics, or branched aliphatics, which can be substituted or unsubstituted, any with or without hetero atoms.

Abstract: A TNIK and/or MAP4K4 kinase inhibitor can include: Formula A, derivative, prodrug, salt, stereoisomer, tautomer polymorph, or solvate thereof, or having chirality at any chiral center, wherein: ring 1 is an aromatic ring with or without hetero atoms; ring 2 is a hetero aromatic ring; ring 3 includes at least one hetero aromatic ring and optionally at least one cycloaliphatic ring fused with the at least one hetero aromatic ring; ring 4 is an aromatic ring with or without hetero atoms; Y is a bond or a linker; Y1 is a linker; each n is independently 0, 1, or 2; each o is independently 0, 1, 2, 3, 4, or 5; each R1, R6, R11, and R12 is independently a substituent; and RA is a ring structure, straight aliphatics, or branched aliphatics, which can be substituted or unsubstituted, any with or without hetero atoms.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: A TLR9 inhibitor includes a compound of general formula (I): wherein the meanings of the variables are explained in the specification, or a stereoisomeric form or a mixture of stereoisomeric forms, or pharmaceutically acceptable salts thereof. A pharmaceutical composition can include compounds of the invention, which can be used in a method for inhibiting TLR9 activity in vitro or in vivo. The method can be performed by administering the compound to a subject to inhibit TLR9 activity, which can be used to treat a disease or disorder associated with TLR9.

Abstract: A TNIK and/or MAP4K4 kinase inhibitor can include: Formula A, derivative, prodrug, salt, stereoisomer, tautomer polymorph, or solvate thereof, or having chirality at any chiral center, wherein: ring 1 is an aromatic ring with or without hetero atoms; ring 2 is a hetero aromatic ring; ring 3 includes at least one hetero aromatic ring and optionally at least one cycloaliphatic ring fused with the at least one hetero aromatic ring; ring 4 is an aromatic ring with or without hetero atoms; Y is a bond or a linker; Y1 is a linker; each n is independently 0, 1, or 2; each o is independently 0, 1, 2, 3, 4, or 5; each R1, R6, R11, and R12 is independently a substituent; and RA is a ring structure, straight aliphatics, or branched aliphatics, which can be substituted or unsubstituted, any with or without hetero atoms.

Abstract: A method is provided for generating new objects having given properties, such as a specific bioactivity (e.g., binding with a specific protein). In some aspects, the method can include: (a) receiving objects (e.g., physical structures) and their properties (e.g., chemical properties, bioactivity properties, etc.) from a dataset; (b) providing the objects and their properties to a machine learning platform, wherein the machine learning platform outputs a trained model; and (c) the machine learning platform takes the trained model and a set of properties and outputs new objects with desired properties. The new objects are different from the received objects. In some aspects, the objects are molecular structures, such as potential active agents, such as small molecule drugs, biological agents, nucleic acids, proteins, antibodies, or other active agents with a desired or defined bioactivity (e.g., binding a specific protein, preferentially over other proteins).

Abstract: A method of inhibiting TNIK and/or MAP4K4 kinase can include: contacting the kinase with a compound of Formula A, wherein: ring 1 is an aromatic ring with or without hetero atoms; ring 2 is a hetero aromatic ring; ring 3 includes at least one hetero aromatic ring and optionally at least one cycloaliphatic ring fused with the at least one hetero aromatic ring; ring 4 is an aromatic ring with or without hetero atoms; Y is a bond or a linker; Y1 is a linker; each n is independently 0, 1, or 2; each o is independently 0, 1, 2, 3, 4, or 5; each R1, R6, R11, and R12 is independently a substituent; and RA is a ring structure, straight aliphatics, or branched aliphatics, which can be substituted or unsubstituted, any with or without hetero atoms.

Abstract: A method of generating molecular structures includes: providing an ABGM; inputting into the ABGM scored molecules having an objective function value; selecting scored molecules with large objective function values; processing the selected scored molecules through an encoder to obtain latent points; selecting a latent point; sampling neighbor latent points that are within a distance from the selected latent point; processing the sampled neighbor latent points with a decoder to generate generated molecules; and provide a report having at least one generated molecule. The scored molecules can have at least one desired property. The method can include: comparing the generated molecules with selected scored molecules; selecting molecules from the generated molecules that are closest to the selected scored molecules; and providing the selected molecules as candidates for having the at least one property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: A method is provided for generating new objects having given properties, such as a specific bioactivity (e.g., binding with a specific protein). In some aspects, the method can include: (a) receiving objects (e.g., physical structures) and their properties (e.g., chemical properties, bioactivity properties, etc.) from a dataset; (b) providing the objects and their properties to a machine learning platform, wherein the machine learning platform outputs a trained model; and (c) the machine learning platform takes the trained model and a set of properties and outputs new objects with desired properties. The new objects are different from the received objects. In some aspects, the objects are molecular structures, such as potential active agents, such as small molecule drugs, biological agents, nucleic acids, proteins, antibodies, or other active agents with a desired or defined bioactivity (e.g., binding a specific protein, preferentially over other proteins).

Abstract: Provided herein are compounds, pharmaceutical compositions and methods for treating a SARS-CoV-2 infection.

Abstract: A method for training model to calculate synthetic accessibility includes: accessing molecule database and obtaining molecule; virtually slicing the molecule into fragments; determining a fragment frequency of fragments; calculating molecular descriptors for the fragments; calculating synthetic difficulty score for the molecule; and storing the synthetic difficulty score in a database. A method of evaluating molecular synthetic accessibility includes: selecting target molecule; decomposing the target molecule into molecular fragments; calculating a synthetic difficulty score for the molecular fragments for the target molecule; determining a sum of synthetic difficulty scores for the molecular fragments; determining a fragment density of the molecular fragments; calculating the synthetic accessibility score from the sum of synthetic difficulty scores and fragment densities; and providing the synthetic accessibility score for the target molecule.

Abstract: The disclosure provides TNIK and/or MAP4K4 kinases inhibitors for the treatment of disease. In one aspect, disclosed herein are kinase inhibitors having a structure of Formula (A), (A*), (I), (IIA), or (IIB). Further described herein are pharmaceutical composition comprising these compounds and methods of using these compounds. In one aspect, disclosed herein are methods of treating a disease or condition by administering the kinases inhibitors described herein.

Abstract: The disclosure provides TNIK and/or MAP4K4 kinases inhibitors for the treatment of disease. In one aspect, disclosed herein are kinase inhibitors having a structure of Formula (A), (A*), (I), (IIA), or (IIB). Further described herein are pharmaceutical composition comprising these compounds and methods of using these compounds. In one aspect, disclosed herein are methods of treating a disease or condition by administering the kinases inhibitors described herein.

Abstract: A graph-to-sequence (G2S) architecture is configured to use graph data of objects to generate sequence data of new objects. The process can be used with objects types that can be represented as graph data and sequence data. For instance, such data is molecular data, where each molecule can be represented as molecular graph and in SMILES. Examples also include popular tasks in deep learning of image-to-text or/and image-to-speech translations. Images can be naturally represented as graphs, while text and speech can be natively represented as sequences. The G2S architecture can include a graph encoder and sample generator that produce latent data in a latent space, which latent data can be conditioned with properties of the object. The latent data is input into a discriminator to obtain real or fake objects, and input into a decoder for generating the sequence data of the new objects.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: A computer-implemented method for a generative adversarial approach for conformational space modeling of molecules is provided. The method can include obtaining molecule graph data for a molecule and inputting the molecule graph data into a machine learning platform. The machine learning platform can include architecture of a molecular graph generator, conformation discriminator, stochastic encoder, and latent variables discriminator. The method can include generating a plurality of conformations for the molecule with the machine learning platform. The plurality of conformations are specific to the molecule. Each conformation can have internal coordinates defining positions of atoms of the molecule. At least one conformation for the molecule can be selected based on at least one parameter related to molecular conformations. A report can be prepared that includes the selected at least one conformation for the molecule.

Abstract: The disclosure provides TNIK and/or MAP4K4 kinases inhibitors for the treatment of disease. In one aspect, disclosed herein are kinase inhibitors having a structure of Formula (A), (A*), (I), (IIA), or (IIB). Further described herein are pharmaceutical composition comprising these compounds and methods of using these compounds. In one aspect, disclosed herein are methods of treating a disease or condition by administering the kinases inhibitors described herein.