Methods for training molecular binding models, methods for screening molecules, apparatuses, computer devices and storage media

A computing device, comprising one or more processors and one or more memories, the one or more memories storing at least one computer program, the at least one computer program being loaded and executed by the one or more processors to implement a method for training molecular binding models, the method comprising:

• obtaining protein feature information of sample protein molecules and molecular feature information of sample alternative molecules;
• inputting the protein feature information and the molecular feature information into a molecular binding model, and using the molecular binding model to determine binding activity feature information, embedding feature information and eutectic feature information between the sample protein molecule and the sample alternative molecule, the binding activity feature information characterizing activities of the sample protein molecule and the sample alternative molecule after virtual binding, the embedding feature information characterizing a degree of binding between the sample protein molecule and the sample alternative molecule, and the eutectic feature information characterizing whether a eutectic structure exists between the sample protein molecule and the sample alternative molecule;
• determining a training loss of the molecular binding model based on the binding activity feature information, the embedding feature information and the eutectic feature information;
• and
• outputting the molecular binding model as a trained molecular binding model when the training loss of the molecular binding model meets a training target.

A non-transitory computer readable storage medium, storing at least one computer program, the at least one computer program being loaded and executed by a processor to implement a method for training molecular binding models, the method comprising:

• obtaining protein feature information of sample protein molecules and molecular feature information of sample alternative molecules;
• inputting the protein feature information and the molecular feature information into a molecular binding model, and using the molecular binding model to determine binding activity feature information, embedding feature information and eutectic feature information between the sample protein molecule and the sample alternative molecule, the binding activity feature information characterizing activities of the sample protein molecule and the sample alternative molecule after virtual binding, the embedding feature information characterizing a degree of binding between the sample protein molecule and the sample alternative molecule, and the eutectic feature information characterizing whether a eutectic structure exists between the sample protein molecule and the sample alternative molecule;
• determining a training loss of the molecular binding model based on the binding activity feature information, the embedding feature information and the eutectic feature information;
• and
• outputting the molecular binding model as a trained molecular binding model when the training loss of the molecular binding model meets a training target.