A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds, the method comprising: reading an expression describing a structure of the chemical compound for each chemical compound in the set; enumerating one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set; generating training data based on the one or more enumerated combinations for each chemical compound in the set, the training data including one or more values having a size, each value indicating whether or not a corresponding type of the structural element appears at a corresponding position for each combination; and training the neural network based on the training data for the set of the chemical compounds. 2. The method of claim 1, wherein the method further comprises: extracting information related to a structural feature specific to the set of the chemical compounds by finding one or more input units connected to a predetermined intermediate unit in the neural network. 3. The method of claim 2, wherein the set includes a group of chemical compounds having a similar property and the training includes training the neural network so as to enable the neural network to reconstruct the training data, the structural feature being a partial structural feature considered to contribute the similar property. 4. The method of claim 2, wherein the set includes a first group of first chemical compounds having a first similar property and a second group of second chemical compounds having a second similar property, and the training includes training the neural network so as to enable the neural network to discriminate the chemical compounds into the first and second groups, the structural feature being a partial structural feature considered to discriminate between the first and second chemical compounds. 5. The method of claim 2, wherein the extracting includes: analyzing parameters in the trained neural network to find the one or more input units each having a connection to the predetermined intermediate unit stronger than other input units, the one or more input units representing a continuous structure or distant structures. 6. The method of claim 1, wherein the method further comprises: preparing a table having a first axis and a second axis for each chemical compound in the set, the first axis representing positions ranging from a start position to an end position depending on the maximum number of the structural elements among the set of the chemical compounds, and the second axis representing types of the structural elements; setting a first value into a cell designated by each combination of the one or more enumerated combinations, for each chemical compound in the set; and setting a second value into remaining cells, for each chemical compound in the set. 7. The method of claim 1, wherein the method further comprises: augmenting the training data by shifting positions of the structural elements appeared in the expression for the chemical compound to generate one or more additional training data. 8. The method of claim 1, wherein the method further comprises: augmenting the training data by inserting a space between the structural elements connected via a specific element in the expression for the chemical compound to generate one or more additional training data. 9. The method of claim 1, wherein the expression is in a form of human-readable format and the neural network is a neural network that accepts a fixed length input. 10. The method of claim 1, wherein the expression is a string in a form of SMILES expression, the structural element corresponds to one or more symbols in the string, and the neural network is a feed forward neural network. 11. The method of claim 1, wherein each structural element represents an atom, a bond, a branching, a stereochemical configuration, and a partial chemical structure. 12. A computer system for training a neural network to capture a structural feature specific to a set of chemical compounds, by executing program instructions, the computer system comprising: a memory tangibly storing the program instructions; and a processor in communications with the memory, wherein the computer system is configured to: read an expression describing a structure of the chemical compound for each chemical compound in the set; enumerate one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set; generate training data based on the one or more enumerated combinations for each chemical compound in the set, the training data including one or more values having a size, each value indicating whether or not a corresponding type of the structural element appears at a corresponding position for each combination; and train the neural network based on the training data for the set of the chemical compounds. 13. The computer system of claim 12, wherein the computer system is further configured to: extract information related to a structural feature specific to the set of the chemical compounds by finding one or more input units connected to a predetermined intermediate unit in the neural network. 14. The computer system of claim 12, wherein the computer system is further configured to: prepare a table having a first axis and a second axis for each chemical compound in the set, the first axis representing positions ranging from a start position to end position depending on the maximum number of the structural elements among the set of the chemical compounds, and the second axis representing types of the structural element; set a first value into a cell designated by each combination of the one or more enumerated combinations, for each chemical compound in the set; and set a second value into remaining cells, for each chemical compound in the set. 15. The computer system of claim 12, wherein the computer system is further configured to: augment the training data by shifting positions of the structural elements appeared in the expression for the chemical compound to generate one or more additional training data. 16. The computer system of claim 12, wherein the computer system is further configured to: augment the training data by inserting a space between the structural elements connected via a specific element in the expression for the chemical compound to generate one or more additional training data. 17. A computer program product for training a neural network to capture a structural feature specific to a set of chemical compounds, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: reading an expression describing a structure of the chemical compound for each chemical compound in the set; enumerating one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set; generating training data based on the one or more enumerated combinations for each chemical compound in the set, the training data including one or more values having a size, each value indicating whether or not a corresponding type of the structural element appears at a corresponding position for each combination; and training the neural network based on the training data for the set of the chemical compounds. 18. The computer program product of claim 17, wherein the method further comprises: extracting information related to a structural feature specific to the set of the chemical compounds by finding one or more input units connected to a predetermined intermediate unit in the neural network. 19. The computer program product of claim 17, wherein the method further comprises: preparing a table having a first axis and a second axis for each chemical compound in the set, the first axis representing positions ranging from a start position to end position depending on the maximum number of the structural elements among the set of the chemical compounds, and the second axis representing types of the structural elements; setting a first value into a cell designated by each combination of the one or more enumerated combinations, for each chemical compound in the set; and setting a second value into remaining cells, for each chemical compound in the set. 20. The computer program product of claim 1, wherein the method further comprises: augmenting the training data by shifting positions of the structural elements appeared in the expression for the chemical compound to generate one or more additional training data.