AIMC Topic: Machine Learning

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Identify Survived Key Features and Relevant Mechanisms for Designing High-Entropy Carbides via AI or Machine Learning.

Journal of chemical information and modeling
Multielement high-entropy carbides (HECs) provide many opportunities for HECs to obtain optimal combinations of various properties, e.g., high strength and high flexibility, leading to high toughness. However, the multielements significantly increase...

Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.

Journal of chemical information and modeling
A carcinogenicity assessment of possibly carcinogenic chemicals (International Agency for Research on Cancer: IARC class 2B) was conducted using a consensus framework constructed from three complementary machine learning models: BiLSTM with MACCS fin...

Combating Counterfeit Drugs via Machine Learning-Enabled Array Screening of Multilayer Evolutionary Combinatorial Libraries.

ACS applied materials & interfaces
Counterfeit drugs are a global issue that has a serious impact on patient morbidity and mortality. Driven by nonspecific cross-reactivity, sensor arrays enable the concurrent discrimination of structurally related drug molecules. Nevertheless, rapidl...

Construction and multi-omics analysis of ccRCC mitochondrial related gene machine learning model and validate of key gene FKBP10.

International immunopharmacology
BACKGROUND: Clear cell renal cell carcinoma represents the most prevalent histological subtype of renal malignancy Emerging evidence underscores the critical involvement of mitochondrial dysfunction in oncogenesis and tumor progression. In this study...

A Machine Learning-Driven Cyclic Optimizing Strategy for the Construction of Paper-Based Microfluidic Devices in the Early Diagnosis of Periodontitis.

ACS sensors
The lack of effective optimization strategies hinders the optimal performance of paper-based microfluidic analytical devices (μPADs). In this work, a Machine Learning-driven Computer vision-BP Neural Networks-Genetic Algorithm-based Cyclic Optimizing...

TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery.

ACS nano
Artificial intelligence (AI) has the potential to transform nanoparticle development for drug delivery; however, existing strategies typically optimize either material selection or component ratios in isolation. To enable simultaneous optimization of...

Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable Machine Learning.

Environmental science & technology
Ultrafine particles (UFP, < 100 nm) are abundantly emitted by aircraft, but quantifying their contributions to ambient particle number concentrations (PNC) is challenging due to confounding from local traffic and complex interactions between aircraf...

Predicting the Fate and Source of Groundwater PFAS in the Pearl River Delta Region Based on Machine Learning.

Environmental science & technology
Extensive investigations into the increasingly severe contamination of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in groundwater are currently causing high costs and long duration. Machine learning provides useful tools for predicting the o...

A Machine Learning Assisted Tool and Numerical Model for Analyzing Lipid Nanoparticles.

ACS nano
The transfection potency and biological fate of gene-loaded lipid nanoparticles (LNPs) are often determined by their morphological and physicochemical properties. Cryogenic-electron microscopy (cryo-EM) remains the most effective tool to analyze LNP ...

Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery.

Journal of chemical information and modeling
Machine Learning (ML) methods that relate molecular structure to properties are frequently proposed as in silico surrogates for expensive or time-consuming experiments. In small molecule drug discovery, such methods inform high-stakes decisions like ...