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Adam Pecina

Adam Pecina

mail adam.pecinainvalid-email-name@invalid-email-domainuochb.cas.cz
phone +420 220 183 541
Skupina Jana Řezáče
Výpočetní chemie pro návrh léčiv

Profil

Research Experience
06/2021

Researcher, IOCB Prague, Czech Republic

06/2019

Marie Skłodowska-Curie Research Fellow, Istituto Italiano di Tecnologia, Genoa, Italy

01/2018

Postdoctoral Fellow with Dr. Marco De Vivo, Istituto Italiano di Tecnologia, Genoa, Italy

Načíst více
Education
2016

PhD. in Modeling of Chemical Properties of Nano- and Biostructures, Charles University in Prague, Faculty of Science, Czech Republic 

2009

MSc. in Biophysical Chemistry, Palacký University in Olomouc, Faculty of Science, Czech Republic

2007

BSc. in Applied Chemistry, Palacký University in Olomouc, Faculty of Science, Czech Republic

Všechny publikace

Vybrané publikace

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
A. Pecina J. Fanfrlík M. Lepšík J. Řezáč
Nature Communications 15: 1127 (2024)
Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity…
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Všechny publikace

Nejnovější publikace

The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks
C. Kramer J. Chodera K. L. Damm-Ganamet M. K. Gilson J. Günther U. Lessel R. A. Lewis D. Mobley E. Nittinger A. Pecina M. Schapira W. P. Walters
Journal of Chemical Information and Modeling 65 (5): 2180–2190 (2025)
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Extensive targeting of chemical space at the prime side of ketoamide inhibitors of rhomboid proteases by branched substituents empowers their selectivity and potency
K. Bach J. Dohnálek J. Škerlová J. Kuzmík E. Poláchová S. Stanchev P. Majer J. Fanfrlík A. Pecina J. Řezáč M. Lepšík V. Borshchevskiy V. Polovinkin K. Stříšovský
European Journal of Medicinal Chemistry 275: 116606 (2024)
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SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
A. Pecina J. Fanfrlík M. Lepšík J. Řezáč
Nature Communications 15: 1127 (2024)
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Benchmark Data Sets of Boron Cluster Dihydrogen Bonding for the Validation of Approximate Computational Methods
J. Fanfrlík A. Pecina J. Řezáč M. Lepšík M. B. Sárosi D. Hnyk D. Hnyk P. Hobza
ChemPhysChem 21 (23): 2599-2604 (2020)
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