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© Strand Life Sciences 2006; Confidential04/19/23 1
Combining Natural Language Processing with Substructure Search
for efficient mining of Scientific literature
Shaillay Dogra, Ramesh Hariharan and Kalyanasundaram Subramanian
Strand Life Sciences Pvt. Ltd
© Strand Life Sciences 2006; Confidential04/19/23 2
Background
• During the lead design/optimization phase, only the interaction between the lead and its target is investigated. – Interactions with different targets that could be
potentially undesirable are not studied.
• Undesirable interactions usually only become apparent at later stages of the discovery process - in vivo
© Strand Life Sciences 2006; Confidential04/19/23 3
Solutions that Currently Exist
• Run experimental assays to determine undesirable interactions– A prospective panel of “side-effect” related assays e.g. for
kinases– The need for the assay may arise due to side-effects observed
in animal studies or liabilities known about the target class
– Synthesis and assay costs in conducting these experiments are considerable.
– What has not been checked may be missed
• Run a search engine like “QueryChem” with ‘structure and keyword’– Need to predefine the keyword – results limited by what you
define– Display of results is not intuitive or user-friendly– Further refinement or exploration on these results is unwieldy
© Strand Life Sciences 2006; Confidential04/19/23 4
Justin Klekota, Frederick P. Roth and Stuart L. SchreiberBioinformatics 2006 22(13):1670-1673
• Structures are first searched against public databases
• ‘Text’ names of the ‘hits’ so obtained are then combined with user defined keywords and again used to search information from the internet.
© Strand Life Sciences 2006; Confidential04/19/23 6
Issues with this approach
• Only looks for co-occurrences of the compound and the keyword– Hence, potentially misses lot of interactions
• The result of a search is a (long) text list– not easy to examine – no real analysis is possible
• What could be an alternate approach– Cover as many biological interactions as currently
available in literature– Show results in a user-friendly and intuitive manner– Allow further refinements of search and exploration in a
dynamic manner
© Strand Life Sciences 2006; Confidential04/19/23 7
The Workflow
• ‘Draw’ the structure of a ‘query’ compound,
• Run a similarity or sub-structure search against ‘target’ compounds in an ‘interactions’ database, – define ‘hit’ compounds ‘similar’ to the ‘query’ compound– check the interactions of these ‘hit’ compounds
• A network(s) of interactions for the given compound is obtained…
• Networks can be analyzed - provides a means of understanding the potential liabilities of the scaffold under consideration
© Strand Life Sciences 2006; Confidential04/19/23 8
Basic Assumptions
• Similarity principle– Similar compounds will most likely have similar
biological interactions
• The presence of a pre-mapped interactions database that is remains current with latest literature
• The presence of small molecules within the database along with their structures that affords sub-structure and similarity searching
© Strand Life Sciences 2006; Confidential04/19/23 9
"TLR-2 expression on monocytes was enhanced by macrophage colony-stimulating factor (M-CSF) and interleukin-10 (IL-10), but was reduced by transforming growth factor beta1.
Interaction Database Creation
• Database created using NLP
• Protein, genes and small molecule interactions captured
© Strand Life Sciences 2006; Confidential04/19/23 10
Entity Recognitio
n Phase
Information
Extraction Phase
Input sentence
Tagged sentence
•Glucose-6-phosphatase was found to play a role in the regulation of insulin.
•A was found to play a role in the regulation of B.
•Glucose-6-phosphatase insulinregulation
Interactions
NLP Schemata
© Strand Life Sciences 2006; Confidential04/19/23 11
Entity Recognition
• Create dictionaries of protein names, small molecules etc.
• Identify alternative names/synonyms/symbols
• Resolve ambiguities
© Strand Life Sciences 2006; Confidential04/19/23 12
Information Extraction
• First understand sentence structure– Syntax Analysis
• Understand meaning– Semantic Analysis
• Final interaction extraction– Inferencing
© Strand Life Sciences 2006; Confidential04/19/23 13
Mammal [human, mouse, rat]
Mammal Interaction Database
© Strand Life Sciences 2006; Confidential04/19/23 15
Step 2 - Perform similarity (or) substructure search
© Strand Life Sciences 2006; Confidential04/19/23 18
Analyze Network
• Relevance interactions: binding, transcription, post-translational, small molecules, metabolism or transport regulation etc.
• Interaction networks: shortest path network, network regulators, network targets etc
• Advanced analysis: relevance list, custom relevance interactions, custom interaction network etc
• Enrichment analysis: GO group enrichment, similar pathways etc
• Numerical data analysis: If present
© Strand Life Sciences 2006; Confidential04/19/23 19
Case Study
Potential hepatotoxic side-effects of lead molecules
© Strand Life Sciences 2006; Confidential04/19/23 22
3- Generate Network
Cholestatic Role of Chlorpromazine
© Strand Life Sciences 2006; Confidential04/19/23 23
Processes
hypersensitivity membrane fluidity
portal tract inflammation SAM protection
hepatocyte damage mitochondrial damage
bile acid independent flow bile duct proliferation
bile salt inspissation biliary permeability
pericanalicular microfibrils canalicular dilation
microvilli reduction
Chlorpromazine
Proteins
microsomal enzymes canalicular membrane ATPase
diacylcholine phosphotransferase adenylate cyclase
phospholipase A2 cytochrome p oxidase
sodium-potassium exchanging ATPase
ATP synthase
IL2 Cyp23IP
TNF leucine aminopeptidase3
4- Analyze Biological Processes
© Strand Life Sciences 2006; Confidential04/19/23 25
Hits matching
• Multiple Matches found– including amiodarone
• Network created with a focus on the liver
• Analysis performed on the results
© Strand Life Sciences 2006; Confidential04/19/23 29
Processes
toxic hepatitis portal tract inflammation
fibrosis lipidosis
hepatocyte damage Phospholipidosis
steatosis
Amiodarone
Proteins
phospholipase A2 phospholipase C
Cyp2E1 Cyp3A
CYP3A4
voltage gated potassium channel
SAM domain protein
Table view of interactions and proteins
© Strand Life Sciences 2006; Confidential04/19/23 30
Conclusions
• Combining structure based searches along with an interaction database allows the in silico assessment of the potential liabilities of a lead molecule
• We have performed text-mining using Natural Language Processing (NLP). The approach uses both syntactic and semantic analysis of sentences along with inferencing.
• We have applied NLP on PubMed abstracts to create a database of interactions containing proteins, small molecules and genes
• We can perform similarity and sub-structure searches against this database to generate a network based on hits
• We have demonstrated this approach in two cases to show scaffold liabilities for hepatotoxicity
© Strand Life Sciences 2006; Confidential04/19/23 31
Acknowledgements
• Pathway ArchitectTM Team• SarchitectTM Team
• Vaijayanti Gupta• R. Nalini