QSAR Modeling and Biological Testing of Some 15-LOX Inhibitors in a Series of Homo- and Heterocyclic Compounds by Veronika Khairullina, Yuliya Martynova, Matvey Kanevsky, Irina Kanevskaya, Yurii Zimin, Leonid Maksimov

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Computational studies of some benzothiazinone-pepirazine derivatives and lipase b inhibitor for mycobacterium tuberculosis by Yakubu Ya'u Muhammad, Adamu Uzairu

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QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool by Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen, Linde Schoenmaker, Michiel Jespers, Olivier J. M. Béquignon, Marina Gorostiola González, Remco L. van den Broek, Andrius Bernatavicius, J. G. Coen van Hasselt, Piet. H. van der Graaf, Gerard J. P. van Westen

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Initial Development of Automated Machine Learning-Assisted Prediction Tools for Aryl Hydrocarbon Receptor Activators by Paulina Anna Wojtyło, Natalia Łapińska, Lucia Bellagamba, Emidio Camaioni, Aleksander Mendyk, Stefano Giovagnoli

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IN SILICO ELUCIDATION OF SOME QUINOLINE DERIVATIVES WITH POTENT ANTI-BREAST CANCER ACTIVITIES. by Momohjimoh Idris Ovaku, Stephen Eyije Abech, Gideon Adamu Shallangwa, Adamu Uzairu

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Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study by Thanet Pitakbut, Jennifer Munkert, Wenhui Xi, Yanjie Wei, Gregor Fuhrmann

Subjects: “…Random forest-based QSAR model…”
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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC₅₀ Inhibition Values of FLT3 Tyrosine Kinase by Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz, Paola R. Campodónico

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One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening by James Wellnitz, Sankalp Jain, Joshua E. Hochuli, Travis Maxfield, Eugene N. Muratov, Alexander Tropsha, Alexey V. Zakharov

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