A method for predicting pharmacological effects of a new drug candidate substance performed by a computing device, the method comprising:
- receiving information on a new drug candidate substance;
- selecting a structural similarity type, which is a reference for determining the similarity between substances;
- preparing pharmacological effect prediction models corresponding to the selected structural similarity type from among a plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class;
- and
- predicting whether the new drug candidate substance will have a pharmacological class corresponding to each of the pharmacological effect prediction models based on an output value obtained by inputting information on the new drug candidate substance into each of the prepared pharmacological effect prediction models,
- wherein:
- the structural similarity type is further classified according to which calculation method between a Dice similarity calculation method and a Tanimoto similarity calculation method is to be applied, whether a Bemis-Murcko scaffold is applied, and whether a hydrogen atom bond is applied;
- each of the plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class is created based on machine learning using substances already known whether to have a specific pharmacological class;
- in each of the pharmacologic effect prediction models, a binary vector obtained by using a threshold value for binarization is input as a feature vector of the new drug candidate substance to a similarity calculated according to the selected structural similarity type between the new drug candidate substance and the already known substance;
- the predicting step predicts:
- if a first output value obtained by inputting the feature vector into a first pharmacological effect prediction model corresponding to a first pharmacological class is greater than or equal to a reference value, that the new drug candidate substance has the first pharmacological class;
- if the first output value is less than the reference value, that the new drug candidate substance does not have the first pharmacological class;
- if a second output value obtained by inputting the feature vector into a second pharmacological effect prediction model corresponding to a second pharmacological class is greater than or equal to the reference value, that the new drug candidate substance has the second pharmacological class;
- and
- if the second output value is less than the reference value, that the new drug candidate substance does not have the second pharmacological class.