Author: Mittendorf, Joachim ; Xing, Yi ; Meibom, Daniel ; Krainz, Tanja ; Elowe, Nadine ; Sanchez, Guzman ; Johannes, Sarah ; Henninger, Kerstin ; MacDonald, Bryan ; Torge, Afra ; Beyer, Kristin ; Jungmann, Natalia ; Wasnaire, Pierre ; Menshykau, Denis ; Maassen, Stefanie ; Lindner, Niels ; Cancho-Grande, Yolanda ; Stefan, Eric ; Broehl, Andreas ; Zubov, Dmitry ; Schaefer, Martina

The matrix metalloprotease ADAMTS7 has been identified by multiple genome-wide association studies as being involved in the development of coronary artery disease. Subsequent research revealed the proteolytic function of the enzyme to be relevant for atherogenesis and restenosis after vessel injury. Based on a publicly known dual ADAMTS4/ADAMTS5 inhibitor, we have in silico designed an ADAMTS7 inhibitor of the catalytic domain, which served as a starting point for an optimization campaign. Initially our inhibitors suffered from low selectivity vs MMP12. An X-ray cocrystal structure inspired us to exploit amino acid differences in the binding site of MMP12 and ADAMTS7 to improve selectivity. Further optimization composed of employing 5-membered heteroaromatic groups as hydantoin substituents to become more potent on ADAMTS7. Finally, fine-tuning of DMPK properties yielded BAY-9835, the first orally bioavailable ADAMTS7 inhibitor. Further optimization to improve selectivity vs ADAMTS12 seems possible, and a respective starting point could be identified.

ChemRxiv

Growing and linking optimizers: synthesis-driven molecule design

Author: Descamps, Clarisse ; Garciia, Juan Sanz ; Perron, Quentin ; Tajmouati, Hamza ; Bouttier, Vincent

In the present work, two reaction-based generative models for mol. design are presented: Growing Optimizer and Linking Optimizer.These models are designed to emulate real-life chem. synthesis by sequentially selecting building blocks and simulating the reactions between them to form new compoundsBy focusing on the feasibility of the generated mols., Growing Optimizer and Linking Optimizer offer several advantages, including the ability to restrict chem. to specific building blocks, reaction types, and synthesis pathways, a crucial requirement in drug design.Unlike text-based models, which construct mols. by iteratively forming a textual representation of the mol. structure, and graph-based models, which assemble mols. atom by atom or fragment by fragment, our approach incorporates a more comprehensive understanding of chem. knowledge, making it relevant for drug discovery projects.Comparative anal. with REINVENT 4, a state-of-the-art mol. generative model, shows that Growing Optimizer and Linking Optimizer are more likely to produce synthetically accessible mols. while reaching mols. of interest with the desired properties.

100 Deals associated with BAY-9835

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Indication	Highest Phase	Country/Location	Organization	Date
Atherosclerosis	Preclinical	Germany	Bayer AG	01 Feb 2024
Coronary Disease	Discovery	Germany	Bayer AG	13 Feb 2024

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