Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Martin Dračínský

h-index 2 View on Semantic Scholar

We have 4 of 17 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

Synthesis of Large- and Medium-Sized Heterobiaryl Atropisomeric Rings and Atropopeptides by Catalytic Intramolecular SNAr.

We report the first examples of catalytic, intramolecular N-heterocycle arylation by nucleophilic aromatic substitution (SNAr), a general method for synthesis of highly strained macrocycles and medium-sized heterobiaryl atropisomeric rings. Key to the approach is the ability to generate a strong base catalytically, as...

Alireza Abbasi Kejani, Paritosh Dey, Nicholas H. Stillman et al. · 0 citations
Open access Aug 2026

Beyond a single structure: complementary IR and NMR signatures of multi-minima noncovalent binding

Hydridic hydrogen atoms, where hydrogen carries increased electron density, form a distinct class of noncovalent interactions, yet their structural and energetic characteristics remain poorly understood. Shallow multi-minima potential-energy surfaces and competing low-energy docking motifs make conventional NMR insuffi...

M. Lamanec, M. Marchi, S. H. Hossein Hejazi et al. · 0 citations
Open access Aug 2026

Accurate and Efficient NMR Crystallography through Machine-Learning Geometry Optimization and Shielding Prediction.

We evaluated a fully machine-learning-assisted workflow for NMR crystallography by combining the Universal Model for Atoms (UMA) interatomic potential for crystal structure optimization with the ShiftML3 prediction of solid-state NMR shieldings. Benchmarking against conventional periodic density functional theory (DFT)...

E.v.a. Chaloupecká, O. Socha, Martin Dračínský · 0 citations
Open access Jul 2026

Quantitative Prediction of Exchangeable Proton Chemical Shifts

Accurately predicting NMR chemical shifts of exchangeable protons in solution remains challenging because of the combined influence of solute–solvent interactions and molecular dynamics. We introduce a framework that integrates machine-learning molecular dynamics (ML-MD) with the ShiftML3 machine-learning shielding mod...

O. Socha, Jana Pavlišová, Debashree Manna et al. · 2 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.