The present invention provides a process and method for repurposing existing compounds by leveraging genomic, phenotypic and pharmacological data to cure disease. Applying advanced mathematical analytics using massively interconnected computing capabilities to identify target rich sets of existing compounds available for animal testing at the earliest stage in the process collapses cycle time of development, dramatically reducing costs. Target rich sets obtained through this invention produce compounds or compositions which each have a demonstrated ability to modulate disease or an associated phenotypic expression. By rendering the mechanism of action irrelevant, this invention collapses the time and cost to discovery of an efficacious drug from decades to days and from $Billions to $Millions.

A method is performed by a computer for a preprocessing for calculating binding free energy between a first substance and a second substance. The method includes: obtaining a binding structure of the first substance and the second substance under a condition where the second substance is constrained such that a binding state of the second substance to the first substance is maintained in a predetermined state; and then, based on the obtained binding structure, obtaining the binding structure under a condition where the second substance is not constrained.

A method is performed by a computer for a preprocessing for calculating binding free energy between a first substance and a second substance. The method includes: obtaining a binding structure of the first substance and the second substance under a condition where the second substance is constrained such that a binding state of the second substance to the first substance is maintained in a predetermined state; and then, based on the obtained binding structure, obtaining the binding structure under a condition where the second substance is not constrained.

A system, device, and method for predicting a docked position of a target ligand in a binding site of a biomolecule is disclosed. The prediction makes use of a template ligand-biomolecule complex structure in order to predict a target ligand-biomolecule complex structure. The system and device contain modules allowing for the prediction of a target-ligand biomolecule complex structure. A preparation module can receive information identifying a target ligand and a template ligand-biomolecule structure. A pharmacophore matcher module can identify common pharmacophores between the template ligand and the target ligand. A docking module can predict a docked ligand position of the target ligand by overlapping the pharmacophore models of the target ligand and template ligand while the template ligand is in the binding site of the biomolecule. A biomolecule modification module can modify the biomolecule to reduce clashes between the docked target ligand and the biomolecule.

A system, device, and method for predicting a docked position of a target ligand in a binding site of a biomolecule is disclosed. The prediction makes use of a template ligand-biomolecule complex structure in order to predict a target ligand-biomolecule complex structure. The system and device contain modules allowing for the prediction of a target-ligand biomolecule complex structure. A preparation module can receive information identifying a target ligand and a template ligand-biomolecule structure. A pharmacophore matcher module can identify common pharmacophores between the template ligand and the target ligand. A docking module can predict a docked ligand position of the target ligand by overlapping the pharmacophore models of the target ligand and template ligand while the template ligand is in the binding site of the biomolecule. A biomolecule modification module can modify the biomolecule to reduce clashes between the docked target ligand and the biomolecule.

A system for determining a material to propose to a user is disclosed. The system calculates an availability evaluation value indicating availability for a user of each of materials based on a chemical formula of each of the materials. The system estimates a physical property value of each of the materials based on a chemical formula of each of the materials. The system calculates a physical property evaluation value of each of the materials based on an estimation result of the physical property value of each of the materials. The system calculates an overlooking risk evaluation value indicating priority of presenting each of the materials to the user based on the availability evaluation value and the physical property evaluation value of each of the materials. The system selects a material to present as a candidate material from the materials according to the overlooking risk evaluation value.

A method is disclosed for determining a conformation of a molecule on at least one degree of freedom to optimize according to at least one molecular objective function, the method comprising generating using at least one corresponding degree of freedom to optimize a connected rigid bodies representation for the molecule by identifying a plurality of groups of atoms, generating a data structure representative of the connected rigid bodies representation generating at least one neighborhood for each generated neighborhood of the at least one generated neighborhoods, generating a corresponding binary optimization problem using the data structure, providing the generated corresponding binary optimization problem to a high-performance binary optimizer, obtaining a solution from the high-performance binary optimizer; and providing at least one corresponding solution.

At least one of the disclosed embodiments describes a computer system that enables efficient strain typing by comparing strain k-mer profiles to generate a strain typing relationship mapping. The system may include one or more processors, and one or more hardware storage devices with stored computer-executable instructions. The instructions may cause the computer system to receive a set of nucleotide sequence data. The nucleotide sequence data may include a plurality of nucleotide sequence data structures each corresponding to a separate microbial strain to be analyzed. For each nucleotide sequence data structure, a k-mer profile may be generated. K-mer profiles may be compared to determine a similarity score between the k-mer profiles, which may indicate a relationship mapping of the respective microbial strains corresponding to the k-mer profiles.

Disclosed herein is an automated small molecule generation process for use in in silico drug discovery. The automated process employs a trained neural network that analyzes a graph adjacency tensor which represents a small molecule compound. Over subsequent iterations, the trained neural network analyzes the graph adjacency tensor and predicts actions (e.g., adding an atom, adding a bond type, or assigning a charge) that, if taken, are likely to lead to a valid small molecule compound. Thus, the methods described herein generate small molecule compounds of increased validity in comparison to conventional methodologies.

A method is provided for generating new objects having given properties, such as a specific bioactivity (e.g., binding with a specific protein). In some aspects, the method can include: (a) receiving objects (e.g., physical structures) and their properties (e.g., chemical properties, bioactivity properties, etc.) from a dataset; (b) providing the objects and their properties to a machine learning platform, wherein the machine learning platform outputs a trained model; and (c) the machine learning platform takes the trained model and a set of properties and outputs new objects with desired properties. The new objects are different from the received objects. In some aspects, the objects are molecular structures, such as potential active agents, such as small molecule drugs, biological agents, nucleic acids, proteins, antibodies, or other active agents with a desired or defined bioactivity (e.g., binding a specific protein, preferentially over other proteins).