Metalloenzymes are important therapeutic targets for a variety of human diseases. Computational approaches have recently emerged as effective tools to understand metal-ligand interactions and expand the structural diversity of both metalloenzyme inhibitors (MIs) and metal-binding pharmacophores (MBPs). In this review, we highlight key advances in currently available fine-tuning modeling methods and data-driven cheminformatic approaches.
View Article and Find Full Text PDFThe formation of non-ion conducting byproducts on zinc anode is notoriously detrimental to aqueous zinc-ion batteries (AZIBs). Herein, we successfully transform a representative detrimental byproduct, crystalline zinc hydroxide sulfate (ZHS) to fast-ion conducting solid-electrolyte interphase (SEI) via amorphization and fluorination induced by suspending CaF nanoparticles in dilute sulfate electrolytes. Distinct from widely reported nonhomogeneous organic-inorganic hybrid SEIs that exhibit structural and chemical instability, the designed single-phase SEI is homogeneous, mechanically robust, and chemically stable.
View Article and Find Full Text PDFAlthough large language models (LLMs) have been assessed for general medical knowledge using medical licensing exams, their ability to effectively support clinical decision-making tasks, such as selecting and using medical calculators, remains uncertain. Here, we evaluate the capability of both medical trainees and LLMs to recommend medical calculators in response to various multiple-choice clinical scenarios such as risk stratification, prognosis, and disease diagnosis. We assessed eight LLMs, including open-source, proprietary, and domain-specific models, with 1,009 question-answer pairs across 35 clinical calculators and measured human performance on a subset of 100 questions.
View Article and Find Full Text PDFLarge language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices.
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