A screening system includes control circuitry that determines gene variants present in a compiled genome representative of a subject based on a difference between a reference genome and the compiled genome representative of the subject, and acquires phenotype information from an observation of the subject. The control circuitry further generates multi-dimensional data structure that includes the gene variants in respect of a first dimension, the phenotype information in respect of a second dimension; and a set of data samples in respect of a third dimension. The set of data samples includes the compiled genome sequence representative of the subject, and corresponding historical data samples of other subjects including their corresponding transcript information (for example, including phenotype information) of the other subjects and their gene variants. The control circuitry executes a gene variant interpretation using a correlation function to find phenotype-gene variant relationships based on the generated multi-dimensional data structure.

The present disclosure provides for systems and methods for generating and displaying a three dimensional map of a protein sequence. An exemplary method can provide for using deep learning models to predict protein folding and model protein folding using three dimensional representations. The method more effectively exploits the potential of deep learning approaches. The method approach overall involves three stages—computation, geometry, and assessment.

The present disclosure provides for systems and methods for generating and displaying a three dimensional map of a protein sequence. An exemplary method can provide for using deep learning models to predict protein folding and model protein folding using three dimensional representations. The method more effectively exploits the potential of deep learning approaches. The method approach overall involves three stages—computation, geometry, and assessment.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

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. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.

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. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.

A method for searching for a modification site of a peptide molecule includes: calculating, by a computer, a second steric structure of the peptide molecule by using data of a first steric structure of the peptide molecule, the first steric structure being a steric structure of the peptide molecule in a complex structure of a target molecule and the peptide molecule, the second steric structure being a stable steric structure of the peptide molecule in a state where a steric configuration of a main chain of the peptide molecule in the first steric structure is fixe; and comparing data of the second steric structure with the data of the first steric structure in order to search for a side chain having a difference in steric configuration between the two steric structures.