Method and device for designing target-specific drug in which deep-learning algorithm is combined with water pharmacophore model
AssigneesIncerebro, Inc., Korea University Research and Business Foundation, Sejong Campus
InventorsEun Sung Cho, Minsup Kim, Kichul Park, Wonsang Kim
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Abstract
The present disclosure relates to a method and device for designing a target-specific drug in which a deep learning algorithm is combined with a water pharmacophore model. More particularly, the present disclosure relates to a device and method for generating the library of novel compounds by securing specificity to a target protein through a water pharmacophore (WP) model, and then performing deep learning.
Claims12
A device for designing a target-specific drug, the device comprising:
- a memory to store instructions;
- and
- a processor configured to, by executing the instructions:
- construct a water pharmacophore (WP) model by identifying hydration sites at a binding site of a target protein through molecular dynamics simulation,
- perform a first WP screening on commercially available compound database based on the WP model to screen WP complementary compounds that are capable of complementarily binding to the binding site of the target protein;
- construct a target deep-learning model that is trained specifically to the target protein, by performing deep learning, using the WP complementary compounds as a learning target, on a deep-learning model which encodes an input compound into a latent vector and decodes the latent vector into an output compound;
- generate novel-compound candidates by decoding a sum vector obtained by summing a WP complementary latent vector and a random vector of each of the WP complementary compounds through the target deep-learning model;
- and
- generate a library that is formed specifically to the target protein, by performing a second WP screening on the novel-compound candidates based on the WP model.
A method of designing a target-specific drug, which is performed by a processor executing instructions stored in a memory, the method comprising:
- constructing a water pharmacophore (WP) model by identifying hydration sites at a binding site of a target protein through molecular dynamics simulation;
- performing a first WP screening on commercially available compound database based on the WP model to screen WP complementary compounds that are capable of complementarily binding to the binding site of the target protein;
- constructing a target deep-learning model that is trained specifically to the target protein, by performing deep learning, using the WP complementary compounds as a learning target, on a deep-learning model which encodes an input compound into a latent vector and decodes the latent vector into an output compound;
- generating novel-compound candidates by decoding a sum vector obtained by summing a WP complementary latent vector and a random vector of each of the WP complementary compounds through the target deep-learning model;
- and generating a library that is formed specifically to the target protein, by performing a second WP screening on the novel-compound candidates based on the WP model.
Dependencies are inferred from the claim text.