One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.

The present invention relates to a method for reducing the complexity of bio-crudes. The method includes (a) obtaining experimental data of quantitative and qualitative analyses for the bio-crudes, (b) grouping compounds contained in the bio-crudes according to a predetermined basis based on the experimental data, (c) selecting representative compounds from among the compounds belonging to the same group, and (d) reconstituting the bio-crudes as a mixture of the representative compounds.

The present invention relates to a method for reducing the complexity of bio-crudes. The method includes (a) obtaining experimental data of quantitative and qualitative analyses for the bio-crudes, (b) grouping compounds contained in the bio-crudes according to a predetermined basis based on the experimental data, (c) selecting representative compounds from among the compounds belonging to the same group, and (d) reconstituting the bio-crudes as a mixture of the representative compounds.

A method for predicting retrosynthesis of a compound molecule and a related apparatus. The method includes: obtaining a target molecule and determining the target molecule as a root node in a tree structure, then, expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, further, recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and then, traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. In this way, a retrosynthesis prediction process of a multi-step reaction is realized. Leaf nodes are gradually recursively expanded and screened, to ensure the reliability of reactants determined by the retrosynthesis prediction process of the multi-step reaction, thereby improving the accuracy of prediction of retrosynthesis of compound molecules.

A method for predicting retrosynthesis of a compound molecule and a related apparatus. The method includes: obtaining a target molecule and determining the target molecule as a root node in a tree structure, then, expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, further, recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and then, traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. In this way, a retrosynthesis prediction process of a multi-step reaction is realized. Leaf nodes are gradually recursively expanded and screened, to ensure the reliability of reactants determined by the retrosynthesis prediction process of the multi-step reaction, thereby improving the accuracy of prediction of retrosynthesis of compound molecules.

This disclosure relates generally to system and method for molecular property prediction. Typically, message-pooling mechanism employed in molecular property prediction using conventional message passing neural networks (MPNN) causes over smoothing of the node embeddings of the molecular graph. The disclosed system utilizes edge conditioned identity mapping convolution neural network for the message passing phase. In message passing phase, the system computes an incoming aggregated message vector for each node of the plurality of nodes of the molecular graph based on encoded message received from neighboring nodes such that encoded message vector is generated by fusing a node information and an connecting edge information of the set of neighboring nodes of the node. The incoming aggregated message vector is utilized for computing updated hidden state vector of each node. A discriminative graph-level vector representation is computed by pooling the updated hidden state vectors from all the nodes of the molecular graph.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

The present disclosure relates to a method for optimizing a measurement rate of a field device in a measurement system. The measurement system includes at least one second field device in which a measurement variable of the field device is correlated with the measurement variable of the second field device. The method determines a respective specific correlation pattern between the first measurement variable and the second measurement variable based on a learning phase. This makes it possible to check the measured values from the second field device for the correlation pattern during normal measurement operation and to change the measurement rate of the field device during the corresponding time window. This makes it possible to increase the service life and/or availability in the process installation.