Integrated ML, MD, and DFT Approaches for Identifying TEM β-Lactamase Inhibitors

Aditi Roy's dataset from 2026 contains results from a computational screening study targeting TEM β-lactamase enzymes in Escherichia coli. The data includes evaluations of 3576 antibacterial compounds, with 55 shortlisted for virtual screening based on drug-likeness and non-toxicity. It features molecular docking scores, molecular dynamics simulation results, and density functional theory analyses for a lead compound identified as 344,265.

Use Cases

• Training ML models for binding affinity prediction based on molecular docking scores.
• Validating molecular dynamics simulation protocols using the provided protein-ligand interaction stability data.
• Benchmarking density functional theory calculations against the reported HOMO-LUMO energy gap and molecular electrostatic potential maps.
• Prioritizing compounds for experimental validation in antibiotic resistance research based on the shortlisted 55 candidates.

Strengths

• Dataset is focused on a specific, high-impact biological target (TEM β-lactamase) with clear therapeutic relevance.
• Includes multi-method validation data (docking, MD simulations, DFT) for a lead compound.
• The 55 shortlisted compounds provide a curated starting point for further investigation.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count and specific column-level documentation are unknown, requiring manual inspection after download.
• The dataset is small (140.7 KB), indicating limited scope, likely containing summary results rather than raw screening data.
• Findings are purely computational and require experimental validation, as noted in the description.

Provenance

Source

Aditi Roy via figshare.

Collection Method

Computational screening of compound libraries using molecular docking, dynamics, and density functional theory.

Freshness

Last updated 2026-05-20 05:47:54.