Alexander Hillisch
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Explore the profile of Alexander Hillisch including associated specialties, affiliations and a list of published articles.
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43
Citations
841
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Recent Articles
11.
Wehrhan L, Hillisch A, Mundt S, Tersteegen A, Meier K
ChemMedChem
. 2020 Aug;
15(21):2010-2018.
PMID: 32776472
Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the...
12.
Goller A, Kuhnke L, Montanari F, Bonin A, Schneckener S, Ter Laak A, et al.
Drug Discov Today
. 2020 Jul;
25(9):1702-1709.
PMID: 32652309
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety...
13.
Le Roux A, Blaise E, Boudreault P, Comeau C, Doucet A, Giarrusso M, et al.
J Med Chem
. 2020 May;
63(13):6774-6783.
PMID: 32453569
We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and...
14.
Schneckener S, Grimbs S, Hey J, Menz S, Osmers M, Schaper S, et al.
J Chem Inf Model
. 2019 Nov;
59(11):4893-4905.
PMID: 31714067
Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery...
15.
Follmann M, Briem H, Steinmeyer A, Hillisch A, Schmitt M, Haning H, et al.
Drug Discov Today
. 2018 Dec;
24(3):668-672.
PMID: 30562586
Pharmaceutical companies often refer to 'screening their library' when performing high-throughput screening (HTS) on a corporate compound collection to identify lead structures for small-molecule drug discovery programs. Characteristics of such...
16.
Cavasin A, Hillisch A, Uellendahl F, Schneckener S, Goller A
J Chem Inf Model
. 2018 May;
58(5):1005-1020.
PMID: 29717870
Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on...
17.
Homeyer N, Stoll F, Hillisch A, Gohlke H
J Chem Theory Comput
. 2015 Nov;
10(8):3331-44.
PMID: 26588302
Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance...
18.
Hillisch A, Heinrich N, Wild H
ChemMedChem
. 2015 Sep;
10(12):1958-62.
PMID: 26358802
Computational chemistry within the pharmaceutical industry has grown into a field that proactively contributes to many aspects of drug design, including target selection and lead identification and optimization. While methodological...
19.
Amazit L, Le Billan F, Kolkhof P, Lamribet K, Viengchareun S, Fay M, et al.
J Biol Chem
. 2015 Jul;
290(36):21876-89.
PMID: 26203193
Aldosterone regulates sodium homeostasis by activating the mineralocorticoid receptor (MR), a member of the nuclear receptor superfamily. Hyperaldosteronism leads todeleterious effects on the kidney, blood vessels, and heart. Although steroidal...
20.
Fraczkiewicz R, Lobell M, Goller A, Krenz U, Schoenneis R, Clark R, et al.
J Chem Inf Model
. 2014 Dec;
55(2):389-97.
PMID: 25514239
In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing...