Abstract: A system and method for generating customizable molecular structures for drug discovery. The system includes a processor communicably coupled to a memory and executes a deep neural network based molecular encoding model. The processor receives input datasets of drug-like molecules from private and public databases and are employed as training dataset. The processor further executes a plurality of deep generative models configured to receive input data relating to small molecules which includes desirable molecules and undesirable molecules. The plurality of deep generative models generates molecular structures like the input desirable molecules. The deep neural network based molecular encoding model is configured to map similarities between the molecular structures generated. The deep neural network based molecular encoding model computes intra-model and inter-model distances.

Abstract: A system for generating a novel molecular structure using a protein structure is disclosed. One or more processors generate a protein voxel representation of a protein structure that includes a multichannel three-dimensional (3D) grid that includes a plurality of channels. A cavity region is detected in the protein voxel representation based on a combination of rule-based detection and a deep learning based model. A cavity voxel representation of the cavity region is generated based on upscaling of a regional voxel of the detected cavity region. A ligand voxel representation of a ligand structure is generated based on the cavity voxel representation. A 3D voxel descriptor is determined for a protein-ligand complex based on the protein voxel representation and the ligand voxel representation. A simplified molecular-input line-entry system (SMILES) of a novel molecular structure is generated using a rich 3D embedding vector, which is based on the 3D voxel descriptor.

Abstract: A system and method for entity normalization and disambiguation. The system includes a processor configured to extract entity records pertaining to plurality of entities from one or more data sources; identify connections between the entity records based on common attributes between the entity records; generate a knowledge graph including nodes and edges; determine embeddings of each of the plurality of entity records in a vector space based on meta information and similarities between meta information; determine embeddings of each of the plurality of entity records based on the knowledge graph; determine a proximity score between embeddings of two given entity records in the vector space; and disambiguate the two given entity records using a trained supervised model in an event the proximity score is higher than a predefined threshold.

Abstract: A system for entity disambiguation. The system includes a server arrangement communicably coupled to a database arrangement including a plurality of documents. The server arrangement is configured to generate an entity network by parsing the plurality of documents, determining latent features relating to the plurality of documents and identifying relationships between the latent features and at least one document entity; determine importance score of each of the name entities using centrality algorithms; determine a relevance score of at least one document entity using link analysis algorithms; cluster the name entities based on the relationships therebetween using clustering coefficients; assign a name entity from each cluster of name entities as the person entity; and employ contextual information determined for the entity network to be used in a mathematical model for entity disambiguation.

Abstract: A system for entity disambiguation. The system includes a server arrangement communicably coupled to a database arrangement including a plurality of documents. The server arrangement is configured to generate an entity network by parsing the plurality of documents, determining latent features relating to the plurality of documents and identifying relationships between the latent features and at least one document entity; determine importance score of each of the name entities using centrality algorithms; determine a relevance score of at least one document entity using link analysis algorithms; cluster the name entities based on the relationships therebetween using clustering coefficients; assign a name entity from each cluster of name entities as the person entity; and employ contextual information determined for the entity network to be used in a mathematical model for entity disambiguation.

Abstract: Disclosed is a system for retrieving data records from database arrangement based on search query. The database arrangement comprises plurality of data records that are associated with at least one concept. The system comprises ontological databank and processing arrangement. The ontological databank comprises plurality of concept buckets comprising plurality of concepts. The ontological databank is structured using structuring arrangement communicably coupled to ontological databank. The structuring arrangement comprises classification module to classify plurality of concepts, variant module to generate lexical variants of plurality of concepts, and normalization module to determine validity of plurality of concepts and association of new concept with one of plurality of concept buckets.

Abstract: Disclosed is a system for retrieving data records from database arrangement based on search query. The database arrangement comprises plurality of data records that are associated with at least one concept. The system comprises ontological databank and processing arrangement. The ontological databank comprises plurality of concept buckets comprising plurality of concepts. The ontological databank is structured using structuring arrangement communicably coupled to ontological databank. The structuring arrangement comprises classification module to classify plurality of concepts, variant module to generate lexical variants of plurality of concepts, and normalization module to determine validity of plurality of concepts and association of new concept with one of plurality of concept buckets.

Abstract: Disclosed is system for creating database query from user search query. The system comprises computing device for receiving user search query. The system further comprises processing arrangement communicably coupled to computing device. The processing arrangement comprises query component parser for identifying one or more attributes of user search query. The processing arrangement further comprises one or more component resolution modules. The one or more component resolution modules is operable to receive one or more attributes of user search query; convert user search query into sentence vector; trigger, based on one or more attributes, at least one module from a set of modules; provide sentence vector to triggered at least one module; and receive output from triggered at least one module to obtain database query. Disclosed further is method for creating database query from user search query using aforementioned system.