Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

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 computing method for normalizing molecule graph data for hierarchical molecular generation can include: providing molecule graph data of a molecule having a node; recursively splitting the node into two nodes; iteratively recursively spilling other nodes into two nodes; generating generated molecular graph data of a generated molecule from node splitting; and providing a report with the generated molecular graph. A computing method can include: providing molecule graph data into a latent code generator having multiple levels with a forward and inverse; and generating latent codes by processing molecule graph data through multiple levels of operations, wherein each level of operations has a sequence of sublevels of operations in forward path and inverse path, wherein the sublevels of operations include node merging operation and node splitting operation; generating at least one molecular structure from latent codes; and outputting generate molecule graph data having the at least one molecular structure.

Abstract: A scaffold-oriented line notation can include: a scaffold sequence of atom identifiers of a scaffold, the scaffold sequence includes at least one decoration marker or any number of decoration markers, each decoration marker being adjacent to an atom identifier of a linking atom of the scaffold; a decoration separator following a last atom identifier or a last decoration marker of the scaffold sequence; at least one decoration having at least one atom identifier in a line notation that defines a chemical structure of the chemical moiety of the decoration that is attached to the linking atom of the scaffold of the molecule; in the scaffold sequence, an order of the at least one decoration marker defines an order of the at least one decoration; in the at least one decoration, the first decoration follows the first decoration separator.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.