The Round Robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential
Dimitra-Danai Varsou, Arkaprava Banerjee, Joyita Roy, Kunal Roy, Giannis Savvas, Haralambos Sarimveis, Ewelina Wyrzykowska, Mateusz Balicki, Tomasz Puzyn, Georgia Melagraki, Iseult Lynch and Antreas Afantitis
Beilstein Arch. 2024, 202433. https://lnkd.in/gmH-WX8n
Using a publicly available dataset four research groups (NovaMechanics Ltd (NovaM) - Cyprus, National Technical University of Athens (NTUA) - Greece, QSAR Lab Ltd - Poland, and DTC Laboratory - India) built five distinct machine learning (ML) models for the in silico prediction of the zeta-potential of metal and metal oxide-nanomaterials (NMs) in aqueous medium. The individual models were integrated into a consensus modelling scheme, enhancing their predictive accuracy, and reducing their biases. The consensus models outperform the individual models, resulting in more reliable predictions. We propose this approach as a valuable method for increasing the validity of nanoinformatics models and driving regulatory acceptance of?in silico?new approach methodologies for use within an Integrated Approach to Testing and Assessment (IATA) for risk assessment of NMs.
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