AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity.

Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform, DiZyme, featuring a state-of-the-art database of nanozymes containing 1210 experimental samples, catalytic activity prediction, and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving = 0.

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles.

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs.

Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods.

Magnetic nanoparticles are a prospective class of materials for use in biomedicine as agents for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of nanoparticles with high efficacy is resource-intensive experimental work. In turn, the use of machine learning (ML) methods is becoming useful in materials design and serves as a great approach to designing nanomagnets for biomedicine.

Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data.

Motivation: Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments.

A system suitability testing platform for untargeted, high-resolution mass spectrometry.

The broad coverage of untargeted metabolomics poses fundamental challenges for the harmonization of measurements along time, even if they originate from the very same instrument. Internal isotopic standards can hardly cover the chemical complexity of study samples. Therefore, they are insufficient for normalizing data as done for targeted metabolomics.