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 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 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 method of predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject can include: isolating a plurality of microorganism nucleic acids of microorganisms from a sample of a microbiota of the subject; analyzing the plurality of microorganism nucleic acids to determine an amount of the microorganisms of the microbiota based on the plurality of microorganism nucleic acids; generating a taxonomic profile of the microbiota of the subject based on the amount of each of the microorganisms; processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject; generating a report with the predicted phenotypical age of the subject; and providing the report to the subject.

Abstract: A method of treating senescence in a subject can include applying a senoremediation drug treatment protocol to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment protocol is derived from a computational transcriptome analysis of the tissue or organ of the subject. The method can include applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject. The method can include introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ. The method can include carrying out a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ.

Abstract: A method of treating senescence in a subject can include applying a senoremediation drug treatment protocol to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment protocol is derived from a computational transcriptome analysis of the tissue or organ of the subject. The method can include applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject. The method can include introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ. The method can include carrying out a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a biological data signature derived from a tissue or organ of the subject; (b) creating input vectors based on the biological data signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ. The biological data signature can be based on biological pathway activation signatures for genomics, transcriptomics, proteomics, methylomics, metabolomics, lipidomics, glycomics, or secretomics.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a proteome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the proteome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

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 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 predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject can include: isolating a plurality of microorganism nucleic acids of microorganisms from a sample of a microbiota of the subject; analyzing the plurality of microorganism nucleic acids to determine an amount of the microorganisms of the microbiota based on the plurality of microorganism nucleic acids; generating a taxonomic profile of the microbiota of the subject based on the amount of each of the microorganisms; processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject; generating a report with the predicted phenotypical age of the subject; and providing the report to the subject.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a proteome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the proteome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a transcriptome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the transcriptome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a transcriptome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the transcriptome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of treating senescence in a subject can include applying a senoremediation drug treatment protocol to the subject in order to rescue one or more first cells in the subject, wherein the senoremediation drug treatment protocol is derived from a computational transcriptome analysis of the tissue or organ of the subject. The method can include applying a senolytic drug treatment protocol to the subject in order to remove one or more second cells in the subject. The method can include introducing stem cells into a tissue and/or organ of the subject in order to rejuvenate one or more tissue cells in the tissue and/or one or more organ cells in the organ. The method can include carrying out a reinforcement step that includes one or more actions that prevent further senescence or degradation of the tissue or organ.