Blar i Department of Chemistry på forfatter "Mathai, Neann Sarah"
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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 ... -
Development, validation and application of in-silico methods to predict the macromolecular targets of small organic compounds
Mathai, Neann Sarah (Doctoral thesis, 2021-12-10)Computational methods to predict the macromolecular targets of small organic drugs and drug-like compounds play a key role in early drug discovery and drug repurposing efforts. These methods are developed by building ... -
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 ... -
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 ...