AIMC Topic: Machine Learning

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Synergistic approach utilizing bioinformatics, machine learning, and traditional screening for the identification of novel CSK inhibitors targeting hepatocellular carcinoma.

Journal of computer-aided molecular design
The overexpression or activation of C-terminal Src kinase (CSK) has been recognized as a pivotal factor in the progression of hepatocellular carcinoma (HCC), positioning CSK as a promising therapeutic target. Despite this potential, no CSK-specific i...

Ag+ modified paper-based SERS combined with SiPLS for quantitative detection of soluble As3+ in aqueous realgar solutions.

Analytica chimica acta
The detection of soluble arsenic in realgar and its preparations is crucial for toxicity evaluation. Therefore, surface-enhanced Raman spectroscopy (SERS) combined with machine learning was applied for the rapid detection of soluble As3+ in realgar a...

Unraveling electronic structure modulation mechanism in cobalt spinel Fenton-like catalysis by integrating density functional theory and machine learning.

Water research
Heterogeneous Fenton-like catalysis holds significant promise for environmental remediation, yet conventional trial-and-error methodologies fail to capture the intrinsic structure-activity relationships in catalyst design. Herein, we present an integ...

LUMIR: an LLM-driven unified agent framework for multi-task infrared spectroscopy reasoning.

Analytica chimica acta
Infrared spectroscopy enables rapid and non-destructive characterization of chemical and material properties, yet effective analysis typically requires workflows involving preprocessing, variable selection, and modeling. The construction and optimiza...

Enabling Emergency Response to Arsenic Contamination: Simultaneous and Rapid Identification of Arsenic Speciation by a Machine Learning-Driven Fluorescent Sensor Array.

Environmental science & technology
The rapid identification of arsenic speciation is critical for assessing its toxicity and guiding emergency response during water contamination events, yet it remains a significant challenge for current analytical methods. Herein, a novel machine lea...

Multi-omics and machine learning identify GBP2 as a key therapeutic target of Qingre Kasen granules in lupus nephritis via NF-kappaB modulation.

Renal failure
Lupus nephritis (LN), a severe renal complication of systemic lupus erythematosus (SLE), results from immune abnormalities. Qingre Kasen granules (QS), which have chicory as the main ingredient, play a significant role in treating various inflammator...

Dual-Channel Multiscale Graph Transformer with Adversarial Contrastive Learning and Low-Rank Disentangled Stratified Negative Sampling for Drug Repositioning.

Journal of chemical information and modeling
Drug repositioning accelerates therapeutic discovery, but existing computational methods are hampered by representation collapse, noisy supervision, and suboptimal negative sampling. To address these limitations, we introduce MGTAL-DR, a novel graph ...

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design.

Journal of chemical information and modeling
Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural ...

Prognostic Value of a Coronary Computed Tomography Angiography-Derived Ischemia Algorithm: Comparison Against Hybrid Coronary Computed Tomography Angiography/Positron Emission Tomography Imaging.

Journal of the American Heart Association
BACKGROUND: Artificial intelligence-guided quantitative computed tomography ischemia (AI-QCT) is a novel machine-learning method for predicting myocardial ischemia from coronary computed tomography angiography (CCTA). This observational cohort study ...

Multi-omics-based decoding of circulating biomarkers in amyotrophic lateral sclerosis and risks in environmental toxins.

BMC pharmacology & toxicology
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the interplay of genetic and environmental factors, and currently, there there is a lack of effective diagnostic or therapeutic strategies available...