Blar i Bergen Open Research Archive på forfatter "Kirchmair, Johannes"
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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 ... -
Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins
Ehm, Patrick; Lange, Faabiola; Hentschel, Carolin; Jepsen, Anneke; Glück, Madeleine; Nelson, Nina; Bettin, Bettina; de Bruyn Kops, Christina; Kirchmair, Johannes; Nalaskowski, Marcus; Jücker, Manfred (Peer reviewed; Journal article, 2019)Binding of proteins with SH2 domains to tyrosine-phosphorylated signaling proteins is a key mechanism for transmission of biological signals within the cell. Characterization of dysregulated proteins in cell signaling ... -
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 ... -
Computational methods and tools to predict cytochrome P450 metabolism for drug discovery
Tyzack, Jonathan; Kirchmair, Johannes (Peer reviewed; Journal article, 2019)In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP ... -
Conformator: A Novel Method for the Generation of Conformer Ensembles
Friedrich, Nils-Ole; Flachsenberg, Florian; Meyder, Agnes; Sommer, Kai; Kirchmair, Johannes; Rarey, Matthias (Journal article; Peer reviewed, 2019)Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we ... -
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 ... -
Natural products against acute respiratory infections: Strategies and lessons learned
Langeder, Julia; Grienke, Ulrike; Chen, Ya; Kirchmair, Johannes; Rollinger, Judith; Schmidtke, Michaela (Peer reviewed; Journal article, 2020)Ethnopharmacological relevance: A wide variety of traditional herbal remedies have been used throughout history for the treatment of symptoms related to acute respiratory infections (ARIs). Aim of the review: The present ... -
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. ... -
Phenylethylene glycol-derived LpxC inhibitors with diverse Zn2+-binding groups
Galster, Magdalena; Loeppenberg, Marius; Galla, Fabian; Börgel, Frederik; Agoglitta, Oriana; Kirchmair, Johannes; Holl, Ralph (Peer reviewed; Journal article, 2019)The Zn2+-dependent bacterial deacetylase LpxC is a promising target for the development of novel antibiotics. Most of the known LpxC inhibitors carry a hydroxamate moiety as Zn2+-binding group. However, hydroxamic acids ... -
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 ... -
Scope of 3D shape-based approaches in predicting the macromolecular targets of structurally complex small molecules including natural products and macrocyclic ligands
Chen, Ya; Mathai, Neann Sarah; Kirchmair, Johannes (Journal article; Peer reviewed, 2020)A plethora of similarity-based, network-based, machine learning, docking and hybrid approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing ... -
Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope
Mathai, Neann Sarah; Kirchmair, Johannes (Journal article; Peer reviewed, 2020-05)Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions ... -
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 ... -
Toxicity prediction using target, interactome, and pathway profiles as descriptors
Füzi, Barbara; Mathai, Neann Sarah; Kirchmair, Johannes; Ecker, Gerhard F. (Journal article; Peer reviewed, 2023)In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. ... -
Validation strategies for target prediction methods
Mathai, Neann Sarah; Chen, Ya; Kirchmair, Johannes (Peer reviewed; Journal article, 2020)Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode ...