Graphic user interface assisted chemical structure generation
AssigneeInternational Business Machines Corporation
InventorSeiji Takeda
Life of this patent
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Abstract
A computer implemented method of generating new chemical compounds is provided. The method includes preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes compressing each of the feature vectors into a relational vector, and mapping each of the relational vectors to a map having at least two dimensions. The method further includes presenting the map on a display device. The method further includes receiving a selection of a position on the map, wherein the position is converted to a new relational vector, and decompressing the new relational vector to a candidate feature vector. The method further includes generating a new chemical structure from the candidate feature vector.
Claims18
A computer implemented method of generating new chemical compounds, comprising:
- preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known;
- compressing each of the feature vectors into a relational vector;
- mapping each of the relational vectors to a map having at least 2 dimensions;
- presenting the map to a user on a display device;
- receiving a selection of a position on the map from the user, wherein the position is converted to a new relational vector, wherein the new relational vector includes a coordinate value, α, representing a dispersion value of a Gaussian distribution from which a compressed feature vector, z, is sampled, and a value, μ, representing a mean value of a Gaussian distribution from which the compressed feature vector, z, is sampled;
- decompressing the new relational vector to a candidate feature vector;
- and
- generating a new chemical structure from the candidate feature vector.
A system for generating new chemical compounds, comprising:
- a display device;
- memory, wherein a data set of a plurality of chemical compounds for which a chemical or physical property is stored in the memory;
- a processor device, wherein the processor device is configured to prepare a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known;
- a chemical structure generator configured to compress each of the feature vectors into a relational vector;
- map each of the relational vectors to a map having at least two dimensions;
- present the map to a user on the display device;
- receive a selection of a position on the map from the user, wherein the position is converted to a new relational vector, wherein the new relational vector includes a coordinate value, σ, representing a dispersion value of a Gaussian distribution from which a compressed feature vector, z, is sampled, and a value, μ, representing a mean value of a Gaussian distribution from which the compressed feature vector, z, is sampled;
- decompress the new relational vector to a candidate feature vector;
- and
- generate a new chemical structure from the candidate feature vector.
A non-transitory computer readable storage medium comprising a computer readable program for generating new chemical compounds, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:
- preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known;
- compressing each of the feature vectors into a relational vector;
- mapping each of the relational vectors to a map having at least two dimensions;
- presenting the map to a user on a display device;
- receiving a selection of a position on the map from the user, wherein the position is converted to a new relational vector, wherein the new relational vector includes a coordinate value, σ, representing a dispersion value of a Gaussian distribution from which a compressed feature vector, z, is sampled, and a value, μ, representing a mean value of a Gaussian distribution from which the compressed feature vector, z, is sampled;
- decompressing the new relational vector to a candidate feature vector;
- and
- generating a new chemical structure from the candidate feature vector.
Dependencies are inferred from the claim text.