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1428445-71-9

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1428445-71-9 Usage

Check Digit Verification of cas no

The CAS Registry Mumber 1428445-71-9 includes 10 digits separated into 3 groups by hyphens. The first part of the number,starting from the left, has 7 digits, 1,4,2,8,4,4 and 5 respectively; the second part has 2 digits, 7 and 1 respectively.
Calculate Digit Verification of CAS Registry Number 1428445-71:
(9*1)+(8*4)+(7*2)+(6*8)+(5*4)+(4*4)+(3*5)+(2*7)+(1*1)=169
169 % 10 = 9
So 1428445-71-9 is a valid CAS Registry Number.

1428445-71-9Downstream Products

1428445-71-9Relevant articles and documents

Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform

Desai, Bimbisar,Dixon, Karen,Farrant, Elizabeth,Feng, Qixing,Gibson, Karl R.,Van Hoorn, Willem P.,Mills, James,Morgan, Trevor,Parry, David M.,Ramjee, Manoj K.,Selway, Christopher N.,Tarver, Gary J.,Whitlock, Gavin,Wright, Adrian G.

, p. 3033 - 3047 (2013/05/22)

Drug discovery faces economic and scientific imperatives to deliver lead molecules rapidly and efficiently. Using traditional paradigms the molecular design, synthesis, and screening loops enforce a significant time delay leading to inefficient use of data in the iterative molecular design process. Here, we report the application of a flow technology platform integrating the key elements of structure-activity relationship (SAR) generation to the discovery of novel Abl kinase inhibitors. The platform utilizes flow chemistry for rapid in-line synthesis, automated purification, and analysis coupled with bioassay. The combination of activity prediction using Random-Forest regression with chemical space sampling algorithms allows the construction of an activity model that refines itself after every iteration of synthesis and biological result. Within just 21 compounds, the automated process identified a novel template and hinge binding motif with pIC50 > 8 against Abl kinase - both wild type and clinically relevant mutants. Integrated microfluidic synthesis and screening coupled with machine learning design have the potential to greatly reduce the time and cost of drug discovery within the hit-to-lead and lead optimization phases.

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