J
Jarrod Walsh
Researcher at AstraZeneca
Publications - 12
Citations - 266
Jarrod Walsh is an academic researcher from AstraZeneca. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 7, co-authored 10 publications receiving 177 citations.
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Journal ArticleDOI
Discovery of Imidazo[1,2-a]pyridine Ethers and Squaramides as Selective and Potent Inhibitors of Mycobacterial Adenosine Triphosphate (ATP) Synthesis.
Subramanyam J. Tantry,Shankar D. Markad,Vikas Shinde,Jyothi Bhat,Gayathri Balakrishnan,Amit K. Gupta,Anisha Ambady,Anandkumar Raichurkar,Chaitanyakumar Kedari,Sreevalli Sharma,Naina Vinay Mudugal,Ashwini Narayan,C. N. Naveen Kumar,Robert Nanduri,Sowmya Bharath,Jitendar Reddy,Vijender Panduga,K.R. Prabhakar,Karthikeyan Kandaswamy,Ramanatha Saralaya,Parvinder Kaur,Neela Dinesh,Supreeth Guptha,Kirsty Rich,David Murray,Helen Plant,Marian Preston,Helen Ashton,Darren Plant,Jarrod Walsh,Peter J. Alcock,Kathryn Naylor,Matthew Collier,James D. Whiteaker,Robert E. McLaughlin,Meenakshi Mallya,Manoranjan Panda,Suresh Rudrapatna,Vasanthi Ramachandran,Radha Shandil,Vasan K. Sambandamurthy,Khisi Mdluli,Christopher B. Cooper,Harvey Rubin,Takahiro Yano,Pravin Iyer,Shridhar Narayanan,Stefan Kavanagh,Kakoli Mukherjee,V. Balasubramanian,Vinayak Hosagrahara,Suresh Solapure,Sudha Ravishankar,Shahul Hameed P +53 more
TL;DR: The discovery of two diverse lead series imidazo[1,2-a]pyridine ethers and squaramides as inhibitors of mycobacterial ATP synthesis is reported, establishing a robust structure-activity relationship of these two scaffolds, resulting in nanomolar potencies in an ATP synthesis inhibition assay.
Journal ArticleDOI
Identification and Characterization of Dual Inhibitors of the USP25/28 Deubiquitinating Enzyme Subfamily.
Jonathan D. Wrigley,Gerald Gavory,Iain Simpson,Marian Preston,Helen Plant,Jenna Bradley,Anne U. Goeppert,Ewelina Rozycka,Gareth M. Davies,Jarrod Walsh,Andrea Valentine,Keeva McClelland,Krzysztofa Ewa Odrzywol,Jonathan Renshaw,Joanna Boros,Jonathan Tart,Lindsey Leach,Thorsten Nowak,Richard A. Ward,Tim Harrison,David M. Andrews +20 more
TL;DR: The discovery of the first reported inhibitors of USP28 are described, which are able to bind to and inhibit USP 28, and while displaying a dual activity against the closest homologue USP25, these inhibitors show a high degree of selectivity over other deubiquitinases (DUBs).
Journal ArticleDOI
Nuisance compounds in cellular assays
Jayme L. Dahlin,Douglas S. Auld,Ina Rothenaigner,Steve Haney,Jonathan Z. Sexton,J. Willem M. Nissink,Jarrod Walsh,Jonathan A. Lee,John Strelow,Francis S. Willard,Lori Ferrins,Jonathan B. Baell,Jonathan B. Baell,Michael A. Walters,Bruce K. Hua,Bruce K. Hua,Kamyar Hadian,Bridget K. Wagner +17 more
TL;DR: In this paper, the authors summarize the collective academic, government, and industry experiences regarding cellular nuisance compounds in more complex cell-based assays and suggest best practices to efficiently address these compounds in complex biological settings.
Journal ArticleDOI
Discovery and Optimization of Allosteric Inhibitors of Mutant Isocitrate Dehydrogenase 1 (R132H IDH1) Displaying Activity in Human Acute Myeloid Leukemia Cells
Stuart Donald Jones,Jonathan T. Ahmet,Kelly Ayton,Matthew Ball,Mark Cockerill,Emma E. Fairweather,Nicola Hamilton,Paul Robert Harper,James R. Hitchin,Allan M. Jordan,Colin Levy,Ruth Lopez,Edward A. McKenzie,Martin J. Packer,Darren Plant,Iain Simpson,Peter B. Simpson,Ian Sinclair,Tim C. P. Somervaille,Helen F. Small,Gary J. Spencer,Graeme J. Thomson,Michael Tonge,Ian D. Waddell,Jarrod Walsh,Bohdan Waszkowycz,Mark Wigglesworth,Daniel H. Wiseman,Donald J. Ogilvie +28 more
TL;DR: A collaborative high throughput screen of 1.35 million compounds against mutant (R132H) isocitrate dehydrogenase IDH1 led to the identification of a novel series of inhibitors, which guided the optimization of the series yielding submicromolar enzyme inhibitors with promising cellular activity.
Journal ArticleDOI
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.
Laurianne David,Laurianne David,Jarrod Walsh,Noé Sturm,Isabella Feierberg,J. Willem M. Nissink,Hongming Chen,Jürgen Bajorath,Ola Engkvist +8 more
TL;DR: A machine‐learning model is developed to predictCIATs for three assay technologies and provides a complementary and wider set of predicted CIATs compared to BSF, a published structure‐independent model, and to the PAINS substructural filters.