Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal-ligand complexes. [PDF]
Benarbia A +3 more
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This review summarizes the transcription factors, repressive chromatin‐modifying complexes, and epigenetic mechanisms that control fetal hemoglobin repression. Notably, many regulators of γ‐globin silencing also function in transcriptional and epigenetic networks that drive cancer, highlighting opportunities to translate advances in hemoglobinopathy ...
Meigen Yu +3 more
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Domain-generalized representation learning for cross-chemical-family toxicity prediction. [PDF]
Mahdi WA, Alhowyan A, Obaidullah AJ.
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Machine learning reveals structural determinants of odor detection thresholds and identifies high-potency food odorants. [PDF]
Hou X +7 more
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Rational Design of Novel Isosteviol-Derived Factor Xa Inhibitors Using Integrated QSAR, Molecular Docking, Molecular Dynamics, and MM/GBSA Analyses. [PDF]
Gordon P +4 more
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Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules. [PDF]
Zeng M, Huai L, Liu T, Liu Y, Jiang X.
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Sequence-Derived and Molecular Descriptors for Interpretable Modeling of Molecular Systems: Insights from Peptide Hemolysis. [PDF]
Medvedeva A +5 more
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Ligand conformational entropy as a regime-switching descriptor of the electrostatic-uptake relationship in nanocarrier-immune cell interactions. [PDF]
Truong QH, Truong XK.
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Despite the number of available chemicals growing exponentially, testing of their toxicological and environmental behavior is often a critical issue and alternative strategies are required. Additionally, there is the need to predict properties of not yet synthesized compounds to reduce the costs of synthesis, selecting only those that have the maximal ...
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