Abstract: Embodiments of this application disclose a training method and an application method for a reaction product prediction model. The method includes constructing a positive sample reactant set and a negative sample reactant set through a first auxiliary network; determining a reaction predicted loss value; constructing a positive sample reaction group set and a negative sample reaction group set through a second auxiliary network; determining a reaction relationship predicted loss value; determining, through a third auxiliary network according to the sample reactant vector and the sample reaction product vector, an atom label and a predicted probability value of an atom in the sample reactant being present in a main product; determining an atom predicted loss value; and training the reaction product prediction model based on the reaction predicted loss value, the reaction relationship predicted loss value, and the atom predicted loss value, to obtain a target reaction product prediction model.

Abstract: A method for predicting a reactant molecule including performing feature extraction on a product molecule to obtain a feature of the product molecule, predicting, based on the feature of the product molecule, a conversion path between the product molecule and a plurality of reactant molecules using a reverse reaction prediction model, editing an edited object indicated by each editing action based on an edited state indicated by each editing action in the editing sequence to obtain a plurality of synthons corresponding to the product molecule, and adding, for each synthon, a motif indicated by each synthon completion action based on at least one synthon completion action corresponding to each synthon in the synthon completion sequence and an interface atom indicated by each synthon completion action in the at least one synthon completion action to obtain a plurality of reactant molecules corresponding to the plurality of synthons.

Abstract: A method for predicting reactant molecules includes obtaining a product molecule, and selecting bonds of the product molecule to be broken to obtain a molecule to be completed, the product molecule defining a compound molecule of a reactant molecule to be predicted. The method further includes applying a molecule completion model to complete the molecule to be completed to obtain a completion result indicating a reactant molecule of the product molecule based on the molecule to be completed. The molecule completion model is obtained by training based on sample compound molecules and sample molecules to be completed obtained by masking sub-structures in the sample compound molecules.

Abstract: 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.

Abstract: A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A privacy protection based training sample generation method includes: generating n d-dimensional transform vectors ? from original data to be mined, wherein the original data comprises m original samples, each original sample includes a d-dimensional original vector x and an output tag value y, m and d being natural numbers, and each transform vector ? is determined by a sum of yx of a plurality of original samples randomly selected from the m original samples; and determining the n transform vectors ? as training samples of a binary classification model.

Abstract: A privacy protection based training sample generation method includes: generating n d-dimensional transform vectors ? from original data to be mined, wherein the original data comprises m original samples, each original sample includes a d-dimensional original vector x and an output tag value y, m and d being natural numbers, and each transform vector ? is determined by a sum of yx of a plurality of original samples randomly selected from the m original samples; and determining the n transform vectors ? as training samples of a binary classification model.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.