Combinatorial discovery of antibacterials <i>via</i> a feature-fusion based machine learning workflow

Wang, Cong; Wu, Yuhui; Xue, Yunfan; Zou, Lingyun; Huang, Yue; Zhang, Peng; Ji, Jian

doi:10.1039/d3sc06441g

Chem. Sci.

2024

DOI: 10.1039/d3sc06441g

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Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow

Cong Wang,

Yuhui Wu,

Yunfan Xue

et al.

Abstract: A combinatorial library of 111 720 candidates was constructed based on Ugi reaction. A latent space constraint neural network model was integrated into the screening workflow to discover antibacterial leads against methicillin-resistant Staphylococcus aureus (MRSA) with alleviated drug resistance.

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Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage

Huang,

Hu,

Qin

et al. 2024

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Carbon superstructures with exquisite morphologies and functionalities show appealing prospects in energy realms, but the systematic tailoring of their microstructures remains a perplexing topic. Here, hydrangea‐shaped heterodiatomic carbon superstructures (CHS) are designed using a solution phase manufacturing route, wherein machine learning workflow is applied to screen precursor‐matched solvent for optimizing solvent‐precursor interaction. Based on the established solubility parameter model and molecular growth kinetics simulation, ethanol as the optimal solvent stimulates thermodynamic solubilization and growth of polymeric intermediates to evoke CHS. Featured with surface‐active motifs and consecutive charge transfer paths, CHS allows high accessibility of zincophilic sites and fast ion migration with low energy barriers. A anion−cation hybrid charge storage mechanism of CHS cathode is disclosed, which entails physical alternate uptake of Zn2+/CF3SO3− ions at electroactive sites and chemical bipedal redox of Zn2+ ions with carbonyl/pyridine motifs. Such a beneficial electrochemistry contributes to all‐round improvement in Zn‐ion storage, involving excellent capacities (231 mAh g−1 at 0.5 A g−1; 132 mAh g−1 at 50 A g−1), high energy density (152 Wh kg−1), and long‐lasting cyclability (100 000 cycles). This work expands the design versatilities of superstructure materials and will accelerate experimental procedures during carbon manufacturing through machine learning in the future.

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Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage

Huang,

Hu,

Qin

et al. 2024

Small

View full text Add to dashboard Cite

show abstract

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Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow

Cited by 1 publication

References 46 publications

Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage

Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage

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