A computer implemented method of generating a chemical structure having a property value close to a preset value by using a neural network comprising a deep neural network and a recurrent neural network, the method comprising: inputting an image of a chemical structure to the deep neural network that generates a property value of a property of the chemical structure, wherein the image of the chemical structure represents structural characteristics of the chemical structure, and wherein the property of the chemical structure is a characteristic possessed by the chemical structure, wherein a plurality of nodes of an input layer of the deep neural network respectively correspond to pixel values constituting the image; determining an expression region (713, 913) for expressing the property in the image, the expression region comprising one or more pixels in the image; and generating a first new chemical structure by modifying a partial structure in the chemical structure of the image, the partial structure corresponding to the expression region; inputting an image for the rrewfirst chemical structure to the deep neural network to output a property value of a particular property and a factor for the first chemical structure; and generating a second chemical structure by changing a partial structure in the first chemical structure, the partial structure corresponding to the expression region, when the property value of the particular property for the first chemical structure is less than a preset value, and storing the first chemical structure when the property value of the particular property for the first chemical structure is equal to or greater than the preset value; characterised in that generating a second chemical structure uses the recurrent neural network, the recurrent neural network receiving as input the factor, in that determining an expression region for expressing the property comprises: determining the expression region for expressing the property in the image by applying a layer-wise relevance propagation technique to the deep neural network; and wherein an activation function applied to a node of the deep neural network is designated as a linear function to apply the LRP technique to the deep neural network, and a mean square error is designated for optimization. inputting an image of a chemical structure to the deep neural network that generates a property value of a property of the chemical structure, wherein the image of the chemical structure represents structural characteristics of the chemical structure, and wherein the property of the chemical structure is a characteristic possessed by the chemical structure, wherein a plurality of nodes of an input layer of the deep neural network respectively correspond to pixel values constituting the image; determining an expression region (713, 913) for expressing the property in the image, the expression region comprising one or more pixels in the image; and generating a first new chemical structure by modifying a partial structure in the chemical structure of the image, the partial structure corresponding to the expression region; inputting an image for the rrewfirst chemical structure to the deep neural network to output a property value of a particular property and a factor for the first chemical structure; and generating a second chemical structure by changing a partial structure in the first chemical structure, the partial structure corresponding to the expression region, when the property value of the particular property for the first chemical structure is less than a preset value, and storing the first chemical structure when the property value of the particular property for the first chemical structure is equal to or greater than the preset value; characterised in that generating a second chemical structure uses the recurrent neural network, the recurrent neural network receiving as input the factor, in that determining an expression region for expressing the property comprises: determining the expression region for expressing the property in the image by applying a layer-wise relevance propagation technique to the deep neural network; and wherein an activation function applied to a node of the deep neural network is designated as a linear function to apply the LRP technique to the deep neural network, and a mean square error is designated for optimization. determining the expression region for expressing the property in the image by applying a layer-wise relevance propagation technique to the deep neural network; and wherein an activation function applied to a node of the deep neural network is designated as a linear function to apply the LRP technique to the deep neural network, and a mean square error is designated for optimization.