Blar i Bergen Open Research Archive på forfatter "Stork, Conrad"
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ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance
Fan, Ningning; Bauer, Christoph; Stork, Conrad; de Bruyn Kops, Christina; Kirchmair, Johannes (Peer reviewed; Journal article, 2019-10)Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ... -
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
Mathai, Neann Sarah; Kirchmair, Johannes; Stork, Conrad (Journal article; Peer reviewed, 2021)Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is ... -
Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop
Ntie-Kang, Fidele; Telukunta, Kiran K.; Fobofou, Serge A. T.; Chukwudi Osamor, Victor; Egieyeh, Samuel A.; Valli, Marilia; Djoumbou-Feunang, Yannick; Sorokina, Maria; Stork, Conrad; Mathai, Neann Sarah; Zierep, Paul; Chávez-Hernández, Ana L.; Duran-Frigola, Miquel; Babiaka, Smith B.; Tematio Fouedjou, Romuald; Eni, Donatus B.; Akame, Simeon; Arreyetta-Bawak, Augustine B.; Ebob, Oyere T.; Metuge, Jonathan A.; Bekono, Boris D.; Isa, Mustafa A.; Onuku, Raphael; Shadrack, Daniel M.; Musyoka, Thommas M.; Patil, Vaishali M.; van der Hooft, Justin J. J.; da Silva Bolzani, Vanderlan; Medina-Franco, José L.; Kirchmair, Johannes; Weber, Tilmann; Tastan Bishop, Özlem; Medema, Marnix H.; Wessjohann, Ludger A.; Ludwig-Müller, Jutta (Journal article; Peer reviewed, 2021-09-06)We report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academia and industry ... -
FAME 3: Predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes
Sicho, Martin; Stork, Conrad; Mazzolari, Angelica; de Bruyn Kops, Christina; Pedretti, Alessandro; Testa, Bernard; Vistoli, Giulio; Svozil, Daniel; Kirchmair, Johannes (Journal article; Peer reviewed, 2019)In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, ... -
GLORY: Generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism
de Bruyn Kops, Christina; Stork, Conrad; Sicho, Martin; Kochev, Nikolay; Svozil, Daniel; Jeliazkova, Nina; Kirchmair, Johannes (Peer reviewed; Journal article, 2019-06-12)Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective ... -
Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters
Stork, Conrad; Chen, Ya; Sicho, Martin; Kirchmair, Johannes (Journal article; Peer reviewed, 2019)Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially ... -
NP-scout: Machine learning approach for the quantification and visualization of the natural product-likeness of small molecules
Chen, Ya; Stork, Conrad; Hirte, Steffen; Kirchmair, Johannes (Peer reviewed; Journal article, 2019)Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. ... -
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
Wilm, Anke; Garcia de Lomana, Marina; Stork, Conrad; Mathai, Neann Sarah; Hirte, Steffen; Norinder, Ulf; Kühl, Jochen; Kirchmair, Johannes (Journal article; Peer reviewed, 2021)In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within ... -
Skin Doctor: Machine learning models for skin sensitization prediction that provide estimates and indicators of prediction reliability
Wilm, Anke; Stork, Conrad; Bauer, Christoph; Schepky, Andreas; Kühnl, Jochen; Kirchmair, Johannes (Peer reviewed; Journal article, 2019-09-28)The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods ...