Method and apparatus for generating chemical structure using neural network

Abstract

Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

What is claimed is: 
     
         1 . A method of generating a chemical structure by using a neural network apparatus, the method comprising:
 inputting an image of a chemical structure to a trained neural network that generates a property value of a property of the chemical structure, the image of the chemical structure representing structural characteristics of the chemical structure and the property of the chemical structure being a characteristic possessed by the chemical structure;   determining an expression region for expressing the property in the image, the expression region comprising one or more pixels in the image; and   generating a new chemical structure by modifying a partial structure in the chemical structure, the partial structure corresponding to the expression region.   
     
     
         2 . The method of  claim 1 , wherein the determining comprises:
 determining the expression region for expressing the property in the image by the trained neural network performing an interpretation process to determine whether the property value is expressed by the partial structure in the chemical structure.   
     
     
         3 . The method of  claim 2 , wherein the determining comprises:
 determining the expression region for expressing the property in the image by applying a layer-wise relevance propagation (LRP) technique to the trained neural network,   wherein an activation function applied to a node of the trained neural network is designated as a linear function to apply the LRP technique to the trained neural network, and a mean square error (MSE) is designated for optimization.   
     
     
         4 . The method of  claim 1 , wherein the generating comprises:
 obtaining pixel values of the one or more pixels of the expression region in the image; and   generating the new chemical structure by applying Gaussian noise to the pixel values of the one or more pixels and modifying the partial structure corresponding to the expression region.   
     
     
         5 . The method of  claim 1 , wherein the expression region comprises a plurality of expression regions expressing the property and the generating comprises:
 obtaining coordinate information in the image corresponding to the plurality of expression regions;   calculating a center point in the image of the plurality of expression regions based on the coordinate information and obtaining a pixel value of the center point; and   generating the new chemical structure by applying Gaussian noise to the pixel value and modifying the partial structure corresponding to the center point.   
     
     
         6 . The method of  claim 1 , wherein the generating comprises:
 generating a new first chemical structure by modifying the partial structure in the chemical structure, the partial structure corresponding to the expression region;   inputting an image for the new first chemical structure to the trained neural network to output a property value of a particular property for the new first chemical structure; and   generating a new second chemical structure by changing a partial structure in the new first chemical structure, the partial structure corresponding to the expression region, when the property value of the particular property for the new first chemical structure is less than a preset value, and storing the new first chemical structure when the property value of the particular property for the new first chemical structure is equal to or greater than the preset value.   
     
     
         7 . A neural network apparatus configured to generate a chemical structure, the neural network apparatus comprising:
 a memory configured to store at least one program; and   a processor configured to control the neural network apparatus to implement a neural network by executing the at least one program, which when the at least one program is executed the processor is configured to:
 input an image of a chemical structure to a trained neural network that generates a property value of a property of the chemical structure, the image of the chemical structure representing structural characteristics of the chemical structure and the property of the chemical structure being a characteristic possessed by the chemical structure, 
 determine an expression region for expressing the property in the image, the expression region comprising one or more pixels in the image, and 
 generate a new chemical structure by modifying a partial structure in the chemical structure, the partial structure corresponding to the expression region. 
   
     
     
         8 . The neural network apparatus of  claim 7 , wherein the processor when the at least one program is executed is further configured to determine the expression region for expressing the property in the image by the trained neural network performing an interpretation process to determine whether the property value is expressed by the partial structure in the chemical structure. 
     
     
         9 . The neural network apparatus of  claim 8 , wherein the processor when the at least one program is executed is further configured to:
 determine the expression region for expressing the property in the image by applying a layer-wise relevance propagation (LRP) technique to the trained neural network; and   designate an activation function applied to a node of the trained neural network as a linear function to apply the LRP technique to the trained neural network and designate a mean square error (MSE) for optimization.   
     
     
         10 . The neural network apparatus of  claim 7 , wherein the processor when the at least one program is executed is further configured to obtain pixel values of the one or more pixels of the expression region in the image and to generate the new chemical structure by applying Gaussian noise to the pixel values of the one or more pixels and modifying the partial structure corresponding to the expression region. 
     
     
         11 . The neural network apparatus of  claim 7 , wherein the expression region comprises a plurality of expression regions expressing the property and the processor when the at least one program is executed is further configured to:
 obtain coordinate information in the image corresponding to the plurality of expression regions;   calculate a center point in the image of the plurality of expression regions based on the coordinate information and obtaining a pixel value of the center point; and   generate the new chemical structure by applying Gaussian noise to the pixel value and modifying the partial structure corresponding to the center point.   
     
     
         12 . The neural network apparatus of  claim 7 , wherein the processor when the at least one program is executed is further configured to:
 generate a new first chemical structure by modifying the partial structure in the chemical structure, the partial structure corresponding to the expression region;   input an image for the new first chemical structure to the trained neural network to output a property value of a particular property for the new first chemical structure; and   generate a new second chemical structure by changing a partial structure in the new first chemical structure, the partial structure corresponding to the expression region, when the property value of the particular property for the new first chemical structure is less than a preset value, and store the new first chemical structure in the memory when the property value of the particular property for the new first chemical structure is equal to or greater than the preset value.   
     
     
         13 . A non-transitory computer-readable recording medium comprising a program, which, when executed by a computer, performs the method of  claim 1 .