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).

Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

In a method of molecular scaffold hopping an interface of a scheduler computer sends instructions, prepared by the scheduler computer, to a job runner computer to perform a plurality of separate computational tasks. Each of the separate computational tasks includes calculating one or more chemical properties for a query molecule or molecules in a library of molecules. One or more of the plurality of separate computational tasks performed on the job runner computer are preemptible computing instances. Status indicators sent from the job runner computer are received by the interface for each of the plurality of separate computational tasks. The indicators are one of: incomplete, completed, or failed computing instances. The interface resends the instructions to the job runner computer that correspond to the separate computational tasks having the failed computing instance indicator to increase fault-tolerance against the separate computational tasks not attaining the completed computing instance indicator.

In a method of molecular scaffold hopping an interface of a scheduler computer sends instructions, prepared by the scheduler computer, to a job runner computer to perform a plurality of separate computational tasks. Each of the separate computational tasks includes calculating one or more chemical properties for a query molecule or molecules in a library of molecules. One or more of the plurality of separate computational tasks performed on the job runner computer are preemptible computing instances. Status indicators sent from the job runner computer are received by the interface for each of the plurality of separate computational tasks. The indicators are one of: incomplete, completed, or failed computing instances. The interface resends the instructions to the job runner computer that correspond to the separate computational tasks having the failed computing instance indicator to increase fault-tolerance against the separate computational tasks not attaining the completed computing instance indicator.

Computer-implemented methods may include accessing a multi-dimensional embedding space that supports relating embeddings of molecules to predicted values of a given property of the molecules. The method may also include identifying one or more points of interest within the embedding space based on the predicted values. Each of the one or more points of interest may include a set of coordinate values within the multi-dimensional embedding space and may be associated with a corresponding predicted value of the given property. The method may further include generating, for each of the one or more points of interest, a structural representation of a molecule by transforming the set of coordinate values included in the point of interest using a decoder network. The method may include outputting a result that identifies, for each of the one or more points of interest, the structural representation of the molecule corresponding to the point of interest.

A system for enabling in-silico phenotypic screening of drugs, the system is communicably coupled to a phenotype ontological databank. The system include a processor communicably coupled to a memory. The processor is configured to receive a name of at least one drug as an input, fetch targets of at least one existing drug that is similar to the at least one drug to obtain a drug target list, determine, phenotypes of the at least one drug based on associations between the targets in the drug target list and the phenotypes, said associations being accessed from the phenotype ontological databank, generate a network comprising the at least one drug, the targets and the phenotypes, determine a plurality of groups of similar phenotypes that belong to similar biological processes or clinical pathologies, and determine expressions of the phenotypes in each of a plurality of tissues, based on gene, protein and tissue expression data accessed from at least one database, determine tissues where the phenotypes of a given group are relevant and diseases associated with the tissues, based on said expressions of the phenotypes, thereby enabling phenotypic screening of the at least one drug.

In various embodiments, a molecule exploration application determines one or more potential drug candidates during a drug discovery process. The molecule exploration application generates derived molecule specifications based on a query molecule specification and edit heuristics. Subsequently, the molecule exploration application performs, via a mapping algorithm, one or more mapping operations on the derived molecule specifications to generate mapped molecule specifications. The molecule exploration application then performs one or more search operations on a mapped catalog of molecules based on the mapped molecule specifications to determine the one or more potential drug candidates. Advantageously, the molecule exploration application can be used to efficiently determine additional drug development candidates during a drug discovery process.

In various embodiments, a molecule exploration application determines one or more potential drug candidates during a drug discovery process. The molecule exploration application generates derived molecule specifications based on a query molecule specification and edit heuristics. Subsequently, the molecule exploration application performs, via a mapping algorithm, one or more mapping operations on the derived molecule specifications to generate mapped molecule specifications. The molecule exploration application then performs one or more search operations on a mapped catalog of molecules based on the mapped molecule specifications to determine the one or more potential drug candidates. Advantageously, the molecule exploration application can be used to efficiently determine additional drug development candidates during a drug discovery process.

The invention encompasses compositions and compounds for inhibiting CoV2 Spike GP and human ACE2 proteins and a 3D pharmacophore model described herein provides the means for rapid, high-throughput virtual screening of potential anti-CoV2 modulators thus facilitating, optimizing and speeding up the search for the discovery of a potent anti-COVID-19 agent and methods of treatment and prevention thereof.