scholarly journals Phenotypic Screening to Identify Small-Molecule Enhancers for Glucose Uptake: Target Identification and Rational Optimization of Their Efficacy

2014 ◽  
pp. n/a-n/a ◽  
Author(s):  
Minseob Koh ◽  
Jongmin Park ◽  
Ja Young Koo ◽  
Donghyun Lim ◽  
Mi Young Cha ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Small Molecule ◽  
Target Identification ◽  
Phenotypic Screening ◽  
Small Molecule Enhancers

Phenotypic Screening to Identify Small-Molecule Enhancers for Glucose Uptake: Target Identification and Rational Optimization of Their Efficacy

2014 ◽  
Vol 126 (20) ◽  
pp. 5202-5206 ◽  
Author(s):  
Minseob Koh ◽  
Jongmin Park ◽  
Ja Young Koo ◽  
Donghyun Lim ◽  
Mi Young Cha ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Small Molecule ◽  
Target Identification ◽  
Phenotypic Screening ◽  
Small Molecule Enhancers

Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators

2021 ◽  
Vol 28 (3) ◽  
pp. 283-299
Author(s):  
Nil Ege ◽  
Habib Bouguenina ◽  
Marianthi Tatari ◽  
Rajesh Chopra
Keyword(s):  
Target Identification ◽  
E3 Ligase ◽  
Phenotypic Screening

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

The Identification of Novel Small Molecule Compounds with Potent Anti-Fibrotic Properties by Phenotypic Screening of Primary Human Stellate Cells

2016 ◽  
Vol 64 (2) ◽  
pp. S712
Author(s):  
C. Belanger ◽  
M. Dubernet ◽  
E. Negro ◽  
R. Darteil ◽  
D.W. Hum ◽  
...  
Keyword(s):  
Small Molecule ◽  
Stellate Cells ◽  
Phenotypic Screening
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