Roman Zubatyuk
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Explore the profile of Roman Zubatyuk including associated specialties, affiliations and a list of published articles.
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Articles
16
Citations
404
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Recent Articles
1.
Hunnisett L, Francia N, Nyman J, Abraham N, Aitipamula S, Alkhidir T, et al.
Acta Crystallogr B Struct Sci Cryst Eng Mater
. 2024 Oct;
PMID: 39418598
A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on...
2.
Hunnisett L, Nyman J, Francia N, Abraham N, Adjiman C, Aitipamula S, et al.
Acta Crystallogr B Struct Sci Cryst Eng Mater
. 2024 Oct;
PMID: 39405196
A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper...
3.
Zubatyuk R, Biczysko M, Ranasinghe K, Moriarty N, Gokcan H, Kruse H, et al.
bioRxiv
. 2024 Jul;
PMID: 39071315
Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known...
4.
Schimunek J, Seidl P, Elez K, Hempel T, Le T, Noe F, et al.
Mol Inform
. 2023 Oct;
43(1):e202300262.
PMID: 37833243
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient...
5.
Fedik N, Nebgen B, Lubbers N, Barros K, Kulichenko M, Li Y, et al.
J Chem Phys
. 2023 Sep;
159(11).
PMID: 37712780
Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions...
6.
Fedik N, Zubatyuk R, Kulichenko M, Lubbers N, Smith J, Nebgen B, et al.
Nat Rev Chem
. 2023 Apr;
6(9):653-672.
PMID: 37117713
Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used...
7.
Zheng P, Zubatyuk R, Wu W, Isayev O, Dral P
Nat Commun
. 2021 Dec;
12(1):7022.
PMID: 34857738
High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted...
8.
Zubatyuk R, Smith J, Nebgen B, Tretiak S, Isayev O
Nat Commun
. 2021 Aug;
12(1):4870.
PMID: 34381051
Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and...
9.
Zubatiuk T, Nebgen B, Lubbers N, Smith J, Zubatyuk R, Zhou G, et al.
J Chem Phys
. 2021 Jul;
154(24):244108.
PMID: 34241371
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity...
10.
Gorb L, Pekh A, Nyporko A, Ilchenko M, Golius A, Zubatiuk T, et al.
J Phys Chem B
. 2020 Sep;
124(42):9343-9353.
PMID: 32975118
We report a comprehensive quantum-chemical study on d(A)·d(T) and d(G)·d(C) DNA mini-helixes and the Dickerson dodecamer d[CGCGAATTCGCG]. The research was performed to model the evolution of the spatial structure of...