Chemoinformatic and machine learning fusion for designing silver nanoparticles with enhanced antimicrobial properties

Predicting antibacterial activity of silver nanoparticles using physicochemical descriptors

Summary Quantitative prediction of antibacterial activity in green-synthesized silver nanoparticles (AgNPs) is critical for developing effective and sustainable antimicrobial agents, particularly against multidrug-resistant bacteria. However, variability in synthesis protocols, characterization methods, and biological...

Predicting the Efficiency of Nanoliposomes in Cancer Treatment Using Machine Learning Approach

EXPLAINABLE MACHINE LEARNING FOR PREDICTING MOLECULAR TOXICITY FROM PHYSICOCHEMICAL AND STRUCTURAL PROPERTIES

This work shows that explainable machine learning offers accurate and interpretable molecular toxicity predictions to enable compound prioritization and computational chemical-safety assessment.

Machine learning and deep learning for predicting photocatalytic degradation efficiency of metronidazole via TiO2/ZnO nanocomposites: a response surface methodology approach

Findings underscore the effectiveness of kernel-based SVR in modeling complex photocatalytic systems with limited data and highlight the critical role of reaction conditions in optimizing degradation performance.

Machine Learning Prediction of Aggregation‐Induced Emission Energies for Organic Luminogens and Metal Complexes Using Physicochemical Descriptors

Aggregation‐induced emission (AIE) has revolutionized the design of photoluminescent materials by enabling strong solid‐state emission from molecularly nonemissive compounds. However, rational prediction of AIE properties remains challenging because photophysical behavior depends not only on molecular structure but a...

An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Staphylococcus aureus Antimicrobial Peptides

An interpretable, honestly benchmarked multi-objective pipeline that optimizes known anti-S.