Gergely Takács

Budapest University of Technology and Economics — Hungary

Computer Science · Physical Sciences

3h-index43citations18works0.02yr avg
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Research Topics

Computational Drug Discovery Methods(13), Machine Learning in Materials Science(8), Chemistry and Chemical Engineering(7), Free Radicals and Antioxidants(3), Radical Photochemical Reactions(2)

Publications18 total

Author response for "A data-driven journey using results from target-based drug discovery for target deconvolution in phenotypic screening"
2025
A data-driven journey using results from target-based drug discovery for target deconvolution in phenotypic screening
RSC Medicinal Chemistry·2025Open Access
Medicinal‐Chemistry‐Driven Approach to 2‐Substituted Benzoxazole–Estradiol Chimeras: Synthesis, Anticancer Activity, and Early ADME Profile
ChemMedChem·2023· 3 cited
DIY Virtual Chemical Libraries - Novel Starting Points for Drug Discovery
ACS Medicinal Chemistry Letters·2023· 3 citedOpen Access
DIY chemical space product file
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
DIY chemical space product file
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Natural Lipid Extracts as an Artificial Membrane for Drug Permeability Assay: In Vitro and In Silico Characterization
Pharmaceutics·2023· 2 citedOpen Access
CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation
Digital Discovery·2022· 17 citedOpen Access
Scavengome of an antioxidant
Vitamins and hormones·2022· 4 cited
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
2022Open Access
Scavengome of an Antioxidant
2022· 2 citedOpen Access
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
2022Open Access
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
2022Open Access
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
2022Open Access
CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.
2022· 2 citedOpen Access
CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization.
2022Open Access
Analysis of the uncharted, druglike property space by self-organizing maps
Molecular Diversity·2021· 10 citedOpen Access
Novel fuzzy approach for ranking test vectors
2012

Frequent Co-authors

Dávid Havasi(8), György Tibor Balogh(8), Rubén Sánchez-García(7), Matthew C. Robinson(7), Alpha A. Lee(7), F. von Delft(7), Charlotte M. Deane(7), Róbert Kiss(3), Márk Sándor(2), Attila Hunyadi(2), Zoltán Szalai(1), Orinhamhe G. Agbadua(1), Ferenc Kovács(1), Ildikó Huliák(1), Hédi Árva(1), Mónika Kiricsi(1), Dóra Erdös(1), Marianna Kocsis(1), Éva Frank(1), Zsolt Dohánics(1)