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

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

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

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 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 DDR1 inhibitor compound can have a structure of Formula A, derivative thereof, prodrug thereof, salt thereof, stereoisomer thereof, tautomer thereof, polymorph thereof, or solvate thereof, or having any chirality at any chiral center, ring A is a ring structure; ring B is a ring structure; the X1, X2, X3, X4, and X5 are each independently a carbon or a hetero atom with or without a substituent; the Y is a linker; and each R1, R2, R3, R5, and R6 is independently a substituent; and each n is an integer, such as from 0 to the maximum number of allowed substituents on the linker or ring, wherein R5 and/or R6 is optionally nothing.

Abstract: A synthesis protocol for a reaction pathway of a target molecule can be determined by: providing target compound data; performing a chemical synthesis search for at least one reaction pathway for the target compound; processing the target compound data through a single-step reaction enumeration algorithm to obtain at least one reaction step of the least one reaction pathway; processing at least one reaction step with the at least one reaction pathway scoring mechanism model to obtain a reaction step score; constructing reaction pathways based on at least one reaction step and at least one reaction step score; providing a selectivity filter having a selectivity criteria; filtering the reaction pathways so that reactions violating the selectivity criteria is filtered out; ranking the reaction pathways; and providing the reaction pathway ranking.

Abstract: Provided herein are compounds of Formulas (I), (II), (III), and (IV) and subformulas thereof, wherein the variables are defined herein. Also provided herein are pharmaceutical compositions comprising a compound of Formula (I), (II), (III), or (IV) and methods of using the compounds, e.g., in the treatment of immune disorders, inflammatory disorders, and cancer.

Abstract: A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures.

Abstract: A DDR1 inhibitor compound can have a structure of Formula A, derivative thereof, prodrug thereof, salt thereof, stereoisomer thereof, tautomer thereof, polymorph thereof, or solvate thereof, or having any chirality at any chiral center, ring A is a ring structure; ring B is a ring structure; the X1, X2, X3, X4, and X5 are each independently a carbon or a hetero atom with or without a substituent; the Y is a linker; and each R1, R2, R3, R5, and R6 is independently a substituent; and each n is an integer, such as from 0 to the maximum number of allowed substituents on the linker or ring, wherein R5 and/or R6 is optionally nothing.

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.