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Matthias R. Bauer

Researcher at Laboratory of Molecular Biology

Publications -  32
Citations -  1452

Matthias R. Bauer is an academic researcher from Laboratory of Molecular Biology. The author has contributed to research in topics: Mutant & Virtual screening. The author has an hindex of 16, co-authored 30 publications receiving 1008 citations. Previous affiliations of Matthias R. Bauer include AstraZeneca & University of Tübingen.

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Lentiviral vector delivery of parkin prevents dopaminergic degeneration in an α-synuclein rat model of Parkinson's disease

TL;DR: Results indicate that parkin gene therapy may represent a promising candidate treatment for PD, with significant reductions in alpha-synuclein-induced neuropathology, and a key role for parkin in the genesis of Lewy bodies.
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Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets.

TL;DR: The previously introduced DEKOIS methodology is improved with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS).
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Put a ring on it: application of small aliphatic rings in medicinal chemistry.

TL;DR: A historical perspective and comparative up to date overview of commonly applied small rings, exemplifying key principles with recent literature examples and potential hazards and liabilities are illustrated and explained.
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2-Sulfonylpyrimidines: Mild alkylating agents with anticancer activity toward p53-compromised cells

TL;DR: Certain activated electrophilic 2-sulfonylpyrimidines are a new class of thiol-reactive anticancer agents that are especially effective in killing cancer cells with mutant or inactivated p53 or impaired reactive oxygen species detoxification and have relatively low cytotoxicity toward normal cells.
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Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes.

TL;DR: This work presents a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes and demonstrates that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity.