Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling.

Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation.

Here, we have developed a new computational drug target prediction method, called CSNAP that is based on chemical similarity networks.

We further coupled CSNAP to a mitotic database and successfully determined the major mitotic drug targets of a diverse compound set identified in a cell-based chemical screen.

Ligand-based approaches, such as similarity ensemble approach (SEA), SuperPred, TargetHunter, HitPick, ChemMapper and others, compare hit compounds to a database of annotated compounds and drug targets of hit compounds are inferred from the targets of the most similar annotated compounds, based on their chemical structure similarity [6—9].

Additionally, subtle structural changes in the functional groups of active molecules can alter their potency and specificity toward drug targets ; thus, analyzing each molecule independently may not offer a coherent SAR for a congeneric series.

This suggests that a more global and systematic analysis of compound bioactivity is required to improve the current state of in-silico drug target prediction.

We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCl-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer.

We visualized the drug target interaction network with NAViGaTOR 2.3.2 [34].

This indicates that some gene targets are overrepresented among significant drugs; these genes may be valuable drug targets for lung cancer.

There are 4 drugs targeting PLA2G4A included in the CMap collection, and all 4 significantly reverse lung cancer gene changes in our analyses: flunisolide, fluocinonide, fluorometholone, and medrysone.

In total, we identified 247 candidate therapeutics, and for many of these we were able to obtain additional compelling evidence from high-throughput NCI-6O data and databases of known drug targets .

However, since drug target databases do not systematically evaluate a range of drug concentrations and off-target effects, this evidence should only be considered preliminary.

Here we introduced nAnno-Lyze a method for drug target interaction prediction that provides structural details at proteome scale.

This example shows the possibility of studying pathways rather than individual proteins as drug targets , which could be even more interesting in complex diseases such as cancer or Alzheimer where multiple factors play a role in the progress of the disease.

Many in silico methods have been published for drug target identification using network approaches [7,8].

Others, named network-based approaches, exploit network properties to provide the drug target interactions and drug repositioning opportunities [11—18].

This example shows not only the capability of the method to find drug targets but also the possibility to explore pathways rather than individual proteins as targets.

However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated.

This study offers an explanation for the difficulties encountered by current drugs targeting IGFIR to reduce glioblastoma cell growth: a secondary mechanism that upregulates HIFloc.

In sum, we have found a possible target in the insulin signaling system that merits exploration as a candidate drug target for glioblastoma patients and other patients with cancers sensitive to the insulin signaling pathway.

To simulate the effect of using different drug targeting factors in glioblastoma, we set each rate constant to 0 separately, modeling the effects of removing each interaction, with the exception of the basal production and degradation of HIFloc.