Abstract: 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).

Abstract: 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).

Abstract: A computer-implemented method for a generative adversarial approach for conformational space modeling of molecules is provided. The method can include obtaining molecule graph data for a molecule and inputting the molecule graph data into a machine learning platform. The machine learning platform can include architecture of a molecular graph generator, conformation discriminator, stochastic encoder, and latent variables discriminator. The method can include generating a plurality of conformations for the molecule with the machine learning platform. The plurality of conformations are specific to the molecule. Each conformation can have internal coordinates defining positions of atoms of the molecule. At least one conformation for the molecule can be selected based on at least one parameter related to molecular conformations. A report can be prepared that includes the selected at least one conformation for the molecule.

Abstract: A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.

Abstract: A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.

Abstract: 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).

Abstract: A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.