Systems and methods for generating repurposing drug candidates for target diseases

A system comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• obtain an input data pertaining to a plurality of drugs, one or more Medical Subject Headings (MeSH) terms and one or more chemical compounds pertaining to one or more articles, a MeSH tree hierarchy, drug-target mapping information and a disease-target mapping information from a therapeutic targets database (TTD);
• process the input data to obtain a list of matched drugs and a list of refined MeSH terms;
• generate a drug-refined MeSH terms frequency matrix based on the list of matched drugs and the list of refined MeSH terms pertaining to the one or more articles;
• perform a topic modeling on the drug-refined MeSH terms frequency matrix to obtain one or more drug-topic conditional probabilities of one or more topics comprised of one or more refined MeSH terms amongst the list of refined MeSH terms for each drug amongst the plurality of drugs;
• apply, a Kullback-Leibler (K-L) divergence on the one or more drug-topic conditional probabilities to obtain a symmetrized pairwise drug-drug distance matrix;
• apply one or more clustering techniques on the symmetrized pairwise drug-drug distance matrix to generate one or more drug clusters;
• analyze each cluster amongst the one or more drug clusters to identify at least a subset of clusters containing one or more known drugs specific to a target disease;
• segregate a plurality of drugs of each cluster in the identified subset of clusters into a known drug group or unknown drug group;
• predict, for each drug in the known drug group and the unknown drug group, one or more targets using an associated chemical information and one or more machine learning algorithms and mapping one or more corresponding indications from the TTD to each of the predicted one or more targets;
• perform a comparison of the predicted one or more targets and the corresponding one or more mapped indications of (i) each drug comprised in the unknown drug group with (ii) each drug comprised in the known drug group to obtain a filtered set of unknown drugs having a similarity with one or more known drugs;
• identify one or more literature documents from a literature database for each unknown drugs amongst the filtered set of unknown drugs based on (i) a list of targets relevant to the target disease obtained from the TTD, and (ii) one or more associated PubMed identifiers;
• and
• generate a set of recommended repurposing drug candidates further comprising one or more unknown drugs having the similarity with the one or more known drugs of the target disease based on the one or more literature documents indicating an effectiveness against a specific target relevant to the target disease.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• obtaining, an input data pertaining to a plurality of drugs, one or more Medical Subject Headings (MeSH) terms and one or more chemical compounds pertaining to one or more articles, a MeSH tree hierarchy, drug-target mapping information and a disease-target mapping information from a therapeutic targets database (TTD);
• processing the input data to obtain a list of matched drugs and a list of refined MeSH terms;
• generating a drug-refined MeSH terms frequency matrix based on the list of matched drugs and the list of refined MeSH terms pertaining to the one or more articles;
• performing a topic modeling on the drug-refined MeSH terms frequency matrix to obtain one or more drug-topic conditional probabilities of one or more topics comprised of one or more refined MeSH terms amongst the list of refined MeSH terms for each drug amongst the plurality of drugs;
• applying a Kullback-Leibler (K-L) divergence on the one or more drug-topic conditional probabilities to obtain a symmetrized pairwise drug-drug distance matrix;
• applying one or more clustering techniques on the symmetrized pairwise drug-drug distance matrix to generate one or more drug clusters;
• analyzing each cluster amongst the one or more drug clusters to identify at least a subset of clusters containing one or more known drugs specific to a target disease;
• segregating a plurality of drugs of each cluster in the identified subset of clusters into a known drug group or unknown drug group;
• predicting, for each drug in the known drug group and the unknown drug group, one or more targets using an associated chemical information and one or more machine learning algorithms and mapping one or more corresponding indications from the TTD to each of the predicted one or more targets;
• performing a comparison of the predicted one or more targets and the corresponding one or more mapped indications of (i) each drug comprised in the unknown drug group with (ii) each drug comprised in the known drug group to obtain a filtered set of unknown drugs having a similarity with one or more known drugs;
• identifying one or more literature documents from a literature database for each unknown drugs amongst the filtered set of unknown drugs based on (i) a list of targets relevant to the target disease obtained from the TTD, and (ii) one or more associated PubMed identifiers;
• and
• generating a set of recommended repurposing drug candidates further comprising one or more unknown drugs having the similarity with the one or more known drugs of the target disease based on the one or more literature documents indicating an effectiveness against a specific target relevant to the target disease.