A method and system is for receiving data representing gene clusters, the gene clusters including one or more genes configured to encode one or more polypeptides or other small molecules; accessing a machine learning model, the machine learning model being trained with a training dataset that associates the gene clusters to structures of one or more small molecules represented in the data; applying the machine learning model to the data representing the gene clusters; identifying, based on applying the machine learning model, one or more monomers associated with at least one gene cluster represented in the data; and determining a structure for a natural product including the one or more monomers.

The present invention is directed to a method for generating a library of antigen binding molecules for screening for binding to an epitope of interest, said method comprising: a. selecting a template antigen-binding molecule from a set of possible template antigen binding molecules wherein said selected template does not specifically bind the epitope of interest but is known to specifically bind another epitope; b. selecting at least one residue position in said template antigen-binding molecule for mutation; and c. selecting at least one variant residue to substitute at the at least one residue position selected in b; such that a library containing a plurality of variants of said template is generated.

The present invention is directed to a method for generating a library of antigen binding molecules for screening for binding to an epitope of interest, said method comprising: a. selecting a template antigen-binding molecule from a set of possible template antigen binding molecules wherein said selected template does not specifically bind the epitope of interest but is known to specifically bind another epitope; b. selecting at least one residue position in said template antigen-binding molecule for mutation; and c. selecting at least one variant residue to substitute at the at least one residue position selected in b; such that a library containing a plurality of variants of said template is generated.

Contemplated compositions and methods are directed to cancer neoepitopes and uses of such neoepitopes, especially to generate synthetic antibodies against neoepitopes that may then be employed in the manufacture of a therapeutic agent. Preferred therapeutic agents will comprise a synthetic antibody against a neoepitope, and most preferably in combination with a cellular or non-cellular component for use as a diagnostic or therapeutic agent.

Contemplated compositions and methods are directed to cancer neoepitopes and uses of such neoepitopes, especially to generate synthetic antibodies against neoepitopes that may then be employed in the manufacture of a therapeutic agent. Preferred therapeutic agents will comprise a synthetic antibody against a neoepitope, and most preferably in combination with a cellular or non-cellular component for use as a diagnostic or therapeutic agent.

Techniques to identify a mechanism of action of a compound using network dysregulation are disclosed herein. An example method can include selecting at least a first interaction involving at least a first gene, determining a first n-dimensional probability density of gene expression levels for the first gene and one or more genes in a control state, determining a second n-dimensional probability density of gene expression levels for the first gene and one or more genes following treatment using at least one compound, estimating changes between the first probability density and the second probability density, and determining whether the estimated changes are statistically significant.

Techniques to identify a mechanism of action of a compound using network dysregulation are disclosed herein. An example method can include selecting at least a first interaction involving at least a first gene, determining a first n-dimensional probability density of gene expression levels for the first gene and one or more genes in a control state, determining a second n-dimensional probability density of gene expression levels for the first gene and one or more genes following treatment using at least one compound, estimating changes between the first probability density and the second probability density, and determining whether the estimated changes are statistically significant.

Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

In one aspect, a method includes generating a design space for a peptide for an application. The generating includes identifying sequences for the peptide, and updating the sequences by determining, for each of the sequences, a respective set of activities pertaining to the application. The updating produces updated sequences each having updated respective activities. The method includes generating, based on the updated sequences, a solution space within the design space. The solution space includes a target subset of the updated sequences. The method includes performing, using a machine learning model to process the solution space, trials to identify a candidate drug compound that represents a sequence having a level of activity that exceeds a threshold level, and determining metrics pertaining to the machine learning model and a second machine learning model that performs the trials.