Conrad Stork

According to our database, Conrad Stork authored at least 5 papers between 2017 and 2021.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2021

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

Fidele Ntie-Kang, Kiran K. Telukunta, Serge A. T. Fobofou, Victor Chukwudi Osamor, Samuel A. Egieyeh, Marilia Valli, Yannick Djoumbou Feunang, Maria Sorokina, Conrad Stork, Neann Mathai, Paul F. Zierep, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Smith B. Babiaka, Romuald Tematio Fouedjou, Donatus B. Eni, Simeon Akame, Augustine B. Arreyetta-Bawak, Oyere T. Ebob, Jonathan A. Metuge, Boris D. Bekono, Mustafa A. Isa, Raphael Onuku, Daniel M. Shadrack, Thommas M. Musyoka, Vaishali M. Patil, Justin J. J. van der Hooft, Vanderlan da Silva Bolzani, José L. Medina-Franco, Johannes Kirchmair, Tilmann Weber, Özlem Tastan Bishop, Marnix H. Medema, Ludger A. Wessjohann, Jutta Ludwig-Müller

J. Cheminformatics, 2021

2020

NERDD: a web portal providing access to in silico tools for drug discovery.

Conrad Stork, Gerd Embruch, Martin Sícho, Christina de Bruyn Kops, Ya Chen, Daniel Svozil, Johannes Kirchmair

Bioinform., 2020

2019

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Conrad Stork, Ya Chen, Martin Sícho, Johannes Kirchmair

J. Chem. Inf. Model., 2019

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes.

Martin Sícho, Conrad Stork, Angelica Mazzolari, Christina de Bruyn Kops, Alessandro Pedretti, Bernard Testa, Giulio Vistoli, Daniel Svozil, Johannes Kirchmair

J. Chem. Inf. Model., 2019

2017

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity.

Martin Sícho, Christina de Bruyn Kops, Conrad Stork, Daniel Svozil, Johannes Kirchmair

J. Chem. Inf. Model., August, 2017