Abstract: Provided is an object determining method performed by a computer device, relating to the technical field of artificial intelligence. The method includes: acquiring index prediction values of objects in a first object set on a preset index respectively; determining, based on index experimental values and object features of the objects in the first object set on the preset index, a mapping relationship between the preset index and the object features; selecting, from the first object set, objects with the index prediction values satisfying index value screening conditions to obtain a second object set; and determining a target object meeting index requirements of the preset index from the second object set based on the mapping relationship.

Abstract: An artificial intelligence-based compound processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product relates to an artificial intelligence technology. The method includes obtaining an active compound for a target protein; performing compound generation processing on an attribute property of the active compound to obtain a first candidate compound; performing molecular docking processing on the active compound and the target protein to obtain molecular docking information respectively corresponding to a plurality of molecular conformations of the active compound; screening the plurality of molecular conformations based on the molecular docking information respectively to identify a second candidate compound corresponding to the active compound; and constructing a compound library for the target protein based on the first candidate compound and the second candidate compound.

Abstract: This disclosure relates to a method and apparatus for training a prediction model. The method includes: obtaining a training sample set; determining a current training sample from the training sample set based on the training sample weights; inputting current target energy characteristics corresponding to the current training sample into a pre-trained prediction model for basic training to obtain a basic prediction model after completing the basic training; updating the training sample weights corresponding to the training samples based on the basic prediction model; and returning to perform the operation of determining the current training sample from the training sample set based on the updated training sample weights until completing model training to obtain a target prediction model.

Abstract: A training method for a neural network includes determining first disassembly paths of a plurality of first molecules, and obtaining a first cost dictionary based on the first disassembly paths of the first molecules. The method also includes determining molecular expression information of second molecules based on the first disassembly paths of the first molecules, and determining a plurality of third molecules from the second molecules, each of the third molecules representing a class of the second molecules. The method further includes obtaining a second cost dictionary based on second disassembly paths of the third molecules, and performing training based on the first cost dictionary and the second cost dictionary to obtain a target neural network. The target neural network being configured to output cost value information corresponding to a target molecule according to input molecular expression information of the target molecule.

Abstract: This disclosure relates to a method and an apparatus for classification model training.