A computer implemented method of generating new chemical compounds is provided. The method includes preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes compressing each of the feature vectors into a relational vector, and mapping each of the relational vectors to a map having at least two dimensions. The method further includes presenting the map on a display device. The method further includes receiving a selection of a position on the map, wherein the position is converted to a new relational vector, and decompressing the new relational vector to a candidate feature vector. The method further includes generating a new chemical structure from the candidate feature vector.

Discovering molecules (which may be known or may never have been cataloged or ever synthesized) that have desired characteristics is addressed using a machine learning approach. As compared to a brute-force search of a database of known molecules, which may not be computationally feasible, the present machine learning approach renders identification of both known and unknown molecules computationally tractable. Furthermore, the computational effort is largely shifted to training of the machine learning system using a database of known molecules, and the generation of molecules to match any particular characteristics requires relatively little computation. The molecules using the present approach may be further studied, for example, with computer-based simulation or after physical synthesis using biological experimentation to ultimately yield useful chemical compounds.

Discovering molecules (which may be known or may never have been cataloged or ever synthesized) that have desired characteristics is addressed using a machine learning approach. As compared to a brute-force search of a database of known molecules, which may not be computationally feasible, the present machine learning approach renders identification of both known and unknown molecules computationally tractable. Furthermore, the computational effort is largely shifted to training of the machine learning system using a database of known molecules, and the generation of molecules to match any particular characteristics requires relatively little computation. The molecules using the present approach may be further studied, for example, with computer-based simulation or after physical synthesis using biological experimentation to ultimately yield useful chemical compounds.

The techniques described herein relate to methods and apparatus for determining a set of reactions to produce a target product. The method includes receiving the target product, executing a graph traversal thread, requesting, via the graph traversal thread, a first set of reactant predictions for the target product, executing a molecule expansion thread, determining, via the molecule expansion thread and a reactant prediction model, the first set of reactant predictions, and storing the first set of reactant predictions as at least part of the set of reactions.

The techniques described herein relate to methods and apparatus for determining a set of reactions to produce a target product. The method includes receiving the target product, executing a graph traversal thread, requesting, via the graph traversal thread, a first set of reactant predictions for the target product, executing a molecule expansion thread, determining, via the molecule expansion thread and a reactant prediction model, the first set of reactant predictions, and storing the first set of reactant predictions as at least part of the set of reactions.

Provided herein are methods of analyzing simulated solvent-mediated molecular interactions. The methods include defining a plurality of three-dimensional (3D) grids of voxels on simulated target molecules solvated with simulated solvent molecules to produce a 3D simulation structure. The simulated solvent molecules include simulated nonionic solvent molecules and simulated ionic solvent molecules. A first 3D grid of the plurality of 3D grids includes a first spatial resolution and is defined on the simulated nonionic solvent molecules. A second 3D grid of the plurality of 3D grids includes a second spatial resolution that differs from the first spatial resolution and is defined on the simulated ionic solvent molecules. Related systems and computer readable media are also provided.

Provided herein are methods of analyzing simulated solvent-mediated molecular interactions. The methods include defining a plurality of three-dimensional (3D) grids of voxels on simulated target molecules solvated with simulated solvent molecules to produce a 3D simulation structure. The simulated solvent molecules include simulated nonionic solvent molecules and simulated ionic solvent molecules. A first 3D grid of the plurality of 3D grids includes a first spatial resolution and is defined on the simulated nonionic solvent molecules. A second 3D grid of the plurality of 3D grids includes a second spatial resolution that differs from the first spatial resolution and is defined on the simulated ionic solvent molecules. Related systems and computer readable media are also provided.

A data processing system configured for computer visualization of drugs for drug interaction information retrieval is disclosed. For each of multiple different substances and using a camera within the mobile or other computing device, imagery of at least one external characteristic of a physical body of the substance is acquired. An identity of each of the multiple different substances is determined based upon the at least one external characteristic from the acquired imagery. Drug interaction data is retrieved for each of the multiple different substances using the determined identities. Drug interaction data for at least one of the multiple different substances is correlated with at least one other of the multiple different substances. At least one generic substance and/or cost information of at least one of the multiple different substances is identified. The correlated drug interaction data, the at least one generic substance, and/or the cost information are displayed.

A data processing system configured for computer visualization of drugs for drug interaction information retrieval is disclosed. For each of multiple different substances and using a camera within the mobile or other computing device, imagery of at least one external characteristic of a physical body of the substance is acquired. An identity of each of the multiple different substances is determined based upon the at least one external characteristic from the acquired imagery. Drug interaction data is retrieved for each of the multiple different substances using the determined identities. Drug interaction data for at least one of the multiple different substances is correlated with at least one other of the multiple different substances. At least one generic substance and/or cost information of at least one of the multiple different substances is identified. The correlated drug interaction data, the at least one generic substance, and/or the cost information are displayed.

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.