Disclosed are methods for identifying bio-molecules with desired properties (or which are most suitable for a round of directed evolution) from complex bio-molecule libraries or sets of such libraries. Some embodiments of the present disclosure provide methods for virtually screening proteins for beneficial properties. Some embodiments of the present disclosure provide methods for virtually screening enzymes for desired activity and/or selectivity for catalytic reactions involving particular substrates. Some embodiments combine screening and directed evolution to design and develop proteins and enzymes having desired properties. Systems and computer program products implementing the methods are also provided.

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

A method for selecting a material having a desired molecular property comprises generating a combinatorial library of molecule structures derived from a core molecular structure, splitting the library into a training set configured to train a graph neural network (GNN) machine learning (ML) model, a test set configured to test the validity of and assess accuracy of the GNN model, and a prediction set where predictions are made using the GNN model, optimizing geometries of the molecular structures, computing excited state energies of the optimized geometries, encoding molecular structure information into a matrix, determining three mutually orthogonal principal axes, transforming spatial coordinates into mutually orthogonal coordinates, constructing a molecular graph with n nodes, feeding the molecular graph into the GNN model as an input, and selecting a material having a suitable desired molecular property based on the output of the GNN model.

A method for selecting a material having a desired molecular property comprises generating a combinatorial library of molecule structures derived from a core molecular structure, splitting the library into a training set configured to train a graph neural network (GNN) machine learning (ML) model, a test set configured to test the validity of and assess accuracy of the GNN model, and a prediction set where predictions are made using the GNN model, optimizing geometries of the molecular structures, computing excited state energies of the optimized geometries, encoding molecular structure information into a matrix, determining three mutually orthogonal principal axes, transforming spatial coordinates into mutually orthogonal coordinates, constructing a molecular graph with n nodes, feeding the molecular graph into the GNN model as an input, and selecting a material having a suitable desired molecular property based on the output of the GNN model.

The invention utilizes virtual screening strategy to seek for current market drugs as anti-schizophrenia therapy drug repurposing. Drug repurposing strategy finds new uses other than the original medical indications of existing drugs. Finding new indications for such drugs will benefit patients who are in needs for a potential new therapy sooner since known drugs are usually with acceptable safety and pharmacokinetic profiles. In this study, repurposing marketed drugs for DAAO inhibitor as new schizophrenia therapy was performed with virtual screening on marketed drugs and its metabolites. The identified and available drugs and compounds were further confirmed with in vitro DAAO enzymatic inhibitory assay.

The invention utilizes virtual screening strategy to seek for current market drugs as anti-schizophrenia therapy drug repurposing. Drug repurposing strategy finds new uses other than the original medical indications of existing drugs. Finding new indications for such drugs will benefit patients who are in needs for a potential new therapy sooner since known drugs are usually with acceptable safety and pharmacokinetic profiles. In this study, repurposing marketed drugs for DAAO inhibitor as new schizophrenia therapy was performed with virtual screening on marketed drugs and its metabolites. The identified and available drugs and compounds were further confirmed with in vitro DAAO enzymatic inhibitory assay.

The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

The present disclosure provides a method of generating compound information in a computing apparatus, the method including obtaining a learning model for information associated with partial structures, obtaining information associated with a source molecule that is a target of a partial structure modification, obtaining information associated with a partial structure set including a plurality of partial structures of the source molecule, selecting, from the partial structures included in the partial structure set, a target partial structure to be modified, obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule.