Integrated ML, MD, and DFT Study for TEM β-Lactamase Inhibitor Candidates

Aditi Roy published a computational screening dataset on 2026-05-20. It contains results from evaluating 3576 antibacterial compounds against TEM β-lactamase enzymes in Escherichia coli. The study shortlisted 55 compounds based on drug-likeness and non-toxicity, with detailed molecular docking, dynamics, and density functional theory analyses for a lead candidate.

Use Cases

• Training ML models for virtual screening based on pharmacokinetic properties and toxicity criteria mentioned in the description.
• Benchmarking molecular docking scores based on reported binding affinities for TEM-1 and TEM-235 enzymes.
• Analyzing protein-ligand interaction stability using the molecular dynamics simulation and MM/PBSA binding energy calculation methods described.
• Studying chemical reactivity for drug design based on the HOMO-LUMO energy gap and molecular electrostatic potential surface mapping results.

Strengths

• Dataset is derived from a structured computational pipeline evaluating 3576 compounds.
• Includes specific, reproducible metrics such as binding affinities of -8.5 kcal/mol and -8.4 kcal/mol for a lead candidate.
• Results are supported by multiple computational methods: molecular docking, dynamics, and density functional theory.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small at 11.3 KB, indicating limited scope.
• Findings are in silico and require experimental validation, as noted in the description.

Provenance

Source

figshare, author Aditi Roy

Collection Method

Computational screening, molecular docking, dynamics simulations, and density functional theory calculations.

Freshness

Last updated 2026-05-20 05:46:02; freshness should be verified.