M
Mahendra Awale
Researcher at University of Bern
Publications - 42
Citations - 1363
Mahendra Awale is an academic researcher from University of Bern. The author has contributed to research in topics: Chemical space & chEMBL. The author has an hindex of 20, co-authored 41 publications receiving 1054 citations. Previous affiliations of Mahendra Awale include Hoffmann-La Roche.
Papers
More filters
Journal ArticleDOI
Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2.
Marion Poirier,Mahendra Awale,Matthias A. Roelli,Guy T. Giuffredi,Lars Ruddigkeit,Lasse Evensen,Amandine Stooss,Serafina Calarco,James B. Lorens,Roch-Philippe Charles,Jean-Louis Reymond +10 more
TL;DR: The value of target‐prediction tools to guide target identification for phenotypic screening hits and significantly expand the rather limited pharmacology of LPAAT‐β inhibitors are illustrated.
Journal ArticleDOI
The Playbooks of Medicinal Chemistry Design Moves.
TL;DR: In this article, a large database of biologically relevant molecules, such as ChEMBL, SureChEMBL or compound collections of pharmaceutical or agrochemical companies, are used as sources of medicinal chemistry information.
Journal ArticleDOI
Optimizing TRPM4 inhibitors in the MHFP6 chemical space.
Clémence Delalande,Mahendra Awale,Matthias Rubin,Daniel Probst,Lijo Cherian Ozhathil,Jürg Gertsch,Hugues Abriel,Jean-Louis Reymond +7 more
TL;DR: A structure-activity relationship (SAR) study of CBA resulting in two new potent analogs of 4-chloro-2-(2-chlorophenoxy)acetamido)benzoic acid becoming the first potent inhibitor of TRPM4, a cation channel implicated in cardiac diseases and prostate cancer.
Journal ArticleDOI
Matched Molecular Series Analysis for ADME Property Prediction.
TL;DR: This work evaluates the power of matched molecular series analysis (MMSA) as an ADME property prediction tool and identifies statistical metrics that allow estimating when MMSA predictions will work, similar to the well-known applicability domain concept in machine learning.
Journal ArticleDOI
Inhibitors of Human Divalent Metal Transporters DMT1 (SLC11A2) and ZIP8 (SLC39A8) from a GDB-17 Fragment Library.
Jonai Pujol-Giménez,Marion Poirier,Sven Bühlmann,Céline Schuppisser,Rajesh Bhardwaj,Mahendra Awale,Ricardo Visini,Sacha Javor,Matthias A. Hediger,Jean-Louis Reymond +9 more
TL;DR: In this paper, the authors search for inhibitors of divalent metal transporters in a library of 1,676 commercially available 3D-shaped fragment-like molecules from the generated database GDB-17, which lists all possible organic molecules up to 17 atoms of C, N, O, S and halogen.