AIMC Topic: Machine Learning

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A quantitative size stability metrics for long-acting suspensions and its prediction with machine learning.

International journal of pharmaceutics
Defining suspension stability can be extremely complex, but beyond critical during the formulation development of nano- and microsuspensions intended for long-acting injectables. As of now, the current practice is based on the trial-and-error approac...

A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen-π Interactions by Training Neural Networks.

Journal of chemical information and modeling
Noncovalent interactions, such as halogen bonds (XB), play a crucial role in molecular recognition and drug design, yet halogen···π contacts remain comparatively underexplored. Here, we report a proof-of-concept QM-AI approach that integrates high-le...

Flexible Porous ACH/Ag Surface-Enhanced Raman Scattering Platform for Sensitive Detection and Machine-Learning-Assisted Classification of Multiple Pathogenic Bacteria.

Analytical chemistry
Pathogenic bacteria pose serious threats to public health and environmental safety. Conventional colony counting, a standard method for bacterial detection, is time-consuming and unsuitable for rapid on-site detection. In this work, a flexible ACH/Ag...

Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun Mass Spectrometry Data Integration.

Analytical chemistry
Machine learning (ML) modeling on mass spectrometry (MS)-based shotgun data facilitates feature selection and disease modeling. However, batch-specific models often struggle with limited transferability and generalizability, necessitating data integr...

Covalent: Interpretable and Discriminative Collective Variables Reveal Ligand-Dependent Switching in Human Cellular Retinol-Binding Protein 2.

Journal of chemical theory and computation
Identifying collective variables (CVs) that are both discriminative and interpretable remains a central challenge for enhanced sampling and mechanistic analysis of biomolecular systems. We present (), a supervised machine learning-based CV discovery...

A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra.

Proceedings of the National Academy of Sciences of the United States of America
Protein misfolding plays a central role in diseases such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and transthyretin amyloidosis (ATTR), often driven by specific aggregation-prone segments such as A and A of amyloid-42 (A42), -Syn...

Income, psychological security, and subjective well-being in urban China: a machine learning analysis with SHAP interpretation.

BMC psychology
BACKGROUND: Subjective well-being has become a core indicator for measuring social progress and policy effectiveness. However, the "Easterlin Paradox" remains prevalent, and this paradox refers to the disconnect between economic growth and improvemen...

Identification and validation of PANoptosis-related biomarkers in Alzheimer's disease via single-cell RNA sequencing and machine learning.

European journal of medical research
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with complex underlying mechanisms. PANoptosis, a newly defined form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis, may play a crucial ...

Association of blood-based DNA methylation of lncRNAs with Alzheimer's disease diagnosis.

Clinical epigenetics
BACKGROUND: DNA methylation has shown great potential in Alzheimer's disease (AD) blood diagnosis. However, the ability of long non-coding RNAs (lncRNAs), which can be modified by DNA methylation, to serve as noninvasive biomarkers for AD diagnosis r...