The disclosure provides methods and systems for identifying a subset of compounds in a plurality of compounds. The identifying includes obtaining, for each compound, a vector including a set of elements, where each element includes a measurement of a different feature of an instance of a cell context upon exposure to the compound. The identifying includes performing the obtaining for a plurality of cell contexts, to obtain a plurality of vectors for each compound across different cell contexts. The identifying includes combining the vectors for each compound to form a combined vector for each compound, thereby forming a plurality of combined vectors representing different compounds. The identifying includes pruning the plurality of compounds to the subset of compounds based on a similarity between respective combined vectors in the plurality of combined vectors corresponding to compounds in the plurality of compounds.

The disclosure provides methods and systems for identifying a subset of compounds in a plurality of compounds. The identifying includes obtaining, for each compound, a vector including a set of elements, where each element includes a measurement of a different feature of an instance of a cell context upon exposure to the compound. The identifying includes performing the obtaining for a plurality of cell contexts, to obtain a plurality of vectors for each compound across different cell contexts. The identifying includes combining the vectors for each compound to form a combined vector for each compound, thereby forming a plurality of combined vectors representing different compounds. The identifying includes pruning the plurality of compounds to the subset of compounds based on a similarity between respective combined vectors in the plurality of combined vectors corresponding to compounds in the plurality of compounds.

A method for processing data for discovering a new drug candidate substance by a data processing apparatus, includes receiving a predetermined search word, extracting at least one biological entity related to the predetermined search word from a big data database (DB), extracting a degree of mutual association between the predetermined search word and the at least one biological entity, generating a first knowledge network in which a plurality of nodes including the predetermined search word and the at least one biological entity are connected according to the degree of mutual association, computing a graph theory index of the first knowledge network, and generating a second knowledge network using some nodes of the plurality of nodes of which the graph theory index is equal to or greater than a threshold value.

A method for processing data for discovering a new drug candidate substance by a data processing apparatus, includes receiving a predetermined search word, extracting at least one biological entity related to the predetermined search word from a big data database (DB), extracting a degree of mutual association between the predetermined search word and the at least one biological entity, generating a first knowledge network in which a plurality of nodes including the predetermined search word and the at least one biological entity are connected according to the degree of mutual association, computing a graph theory index of the first knowledge network, and generating a second knowledge network using some nodes of the plurality of nodes of which the graph theory index is equal to or greater than a threshold value.

A method for processing data for discovering a new drug candidate substance by a data processing apparatus includes receiving at least some of omics levels that make up omics through a user interface, receiving at least some types of mutual association degrees among a plurality of types of mutual association degrees, selecting a DB for the at least some of the omics levels and a DB for the at least some types of mutual association from an omics DB including data for each omics level and data for each type of mutual association, generating a first matrix composed of the DB for the at least some of the omics levels and the DB for the at least some types of mutual association degrees, receiving a predetermined search word through the user interface, extracting a plurality of biological entities, and generating a multi-omics network in which a plurality of nodes.

A structure search apparatus of searching for a stable structure of a compound in which a plurality of compound residues are bonded together includes: a memory; and a processor circuit coupled to the memory, the processor circuit being configured to perform processing, the processing including: executing an interaction potential identification processing configured to identify an interaction potential between a compound residue x and a compound residue y among the plurality of compound residues; executing a steric structure identification processing configured to identify a steric structure of the compound in a three-dimensional lattice space which is a set of lattice points by arranging the plurality of compound residues at lattice points in the three-dimensional lattice space in consideration of the interaction potential identified by the interaction potential identification processing; and in response to an identification result obtained, outputting the identification result indicating the identified steric structure of the compound.

A structure search apparatus of searching for a stable structure of a compound in which a plurality of compound residues are bonded together includes: a memory; and a processor circuit coupled to the memory, the processor circuit being configured to perform processing, the processing including: executing an interaction potential identification processing configured to identify an interaction potential between a compound residue x and a compound residue y among the plurality of compound residues; executing a steric structure identification processing configured to identify a steric structure of the compound in a three-dimensional lattice space which is a set of lattice points by arranging the plurality of compound residues at lattice points in the three-dimensional lattice space in consideration of the interaction potential identified by the interaction potential identification processing; and in response to an identification result obtained, outputting the identification result indicating the identified steric structure of the compound.

A crystal analysis method for a computer to execute a process includes creating a graph that indicates data of repeating unit cell in an ionic crystal and data of an adjacent repeating unit cell that is adjacent to the repeating unit cell; analyzing the ionic crystal based on the graph; and when a number of first intra-cell node that indicates data of an anionic atom bonded to a cationic atom in the repeating unit cell is n, setting a number of second intra-cell node that indicates data of the anionic atom in the repeating unit cell n−1 or less, wherein the data of repeating unit cell includes a plurality of intra-cell nodes that indicate data of atoms in the repeating unit cell, and the plurality of intra-cell nodes include the first intra-cell node and the second intra-cell node.

Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.

Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.