A computer-implemented method for generating a toxicity prediction model of chemical compounds, comprising: a) obtaining a discriminant function by discriminant analysis for classifying a training sample set of chemical compounds, each of which is known to belong to class 1 having a given toxicity or class 2 not having said given toxicity, into said class 1 or class 2, on the basis of a discriminant analysis explanatory variable generated for each individual training sample contained in said training sample set; b) calculating a discriminant score for each training sample by using said obtained discriminant function; c) based on said calculated discriminant score, determining whether said training sample is correctly classified or not; d) determining a misclassified-sample region based on maximum and minimum discriminant scores taken from among misclassified samples in said training sample set so that both edges of the misclassified-sample region are defined respectively by the maximum discriminant score and the minimum discriminant score; e) constructing a new training sample set by extracting misclassified and/or correctly classified samples whose discriminant scores are contained within said misclassified-sample region; f) repeating said a) to e) for said new training sample set; and g) storing, as a toxicity prediction model with respect to said given toxicity for a sample whose toxicity is unknown, a plurality of discriminant functions obtained as a result of said repetition and misclassified-sample region information associated with each of said discriminant functions, wherein when the number of misclassified training samples in said c) is zero, the subsequent processing of d) to f) is not performed. a) obtaining a discriminant function by discriminant analysis for classifying a training sample set of chemical compounds, each of which is known to belong to class 1 having a given toxicity or class 2 not having said given toxicity, into said class 1 or class 2, on the basis of a discriminant analysis explanatory variable generated for each individual training sample contained in said training sample set; b) calculating a discriminant score for each training sample by using said obtained discriminant function; c) based on said calculated discriminant score, determining whether said training sample is correctly classified or not; d) determining a misclassified-sample region based on maximum and minimum discriminant scores taken from among misclassified samples in said training sample set so that both edges of the misclassified-sample region are defined respectively by the maximum discriminant score and the minimum discriminant score; e) constructing a new training sample set by extracting misclassified and/or correctly classified samples whose discriminant scores are contained within said misclassified-sample region; f) repeating said a) to e) for said new training sample set; and g) storing, as a toxicity prediction model with respect to said given toxicity for a sample whose toxicity is unknown, a plurality of discriminant functions obtained as a result of said repetition and misclassified-sample region information associated with each of said discriminant functions,