Comparative Assessment of Free Energy Methods for PARP Inhibitor Selectivity

A comparative assessment of three computational methods—MM/PBSA, Absolute Binding Free Energy, and Umbrella Sampling—for predicting inhibitor selectivity between PARP1 and PARP2 enzymes. The dataset, authored by Alejandro Feito and last updated in April 2026, contains results from evaluating eight clinically relevant PARP inhibitors used in cancers like ovarian, breast, and prostate. The study demonstrates how contact connectivity analysis reveals key residues controlling ligand selectivity, informing the design of more specific cancer therapies.

Use Cases

• Benchmarking free energy calculation methods based on performance against experimental binding affinities for eight PARP inhibitors.
• Analyzing structural contact connectivity to identify key residues driving ligand selectivity between PARP1 and PARP2.
• Informing the design of selective PARP inhibitors for precision oncology based on insights into chemical modifications across ligand chemical space.
• Comparing the speed and accuracy of MM/PBSA, ABFE, and Umbrella Sampling methods for recapitulating selectivity trends.

Strengths

• Focuses on eight clinically relevant PARP enzyme inhibitors used in ovarian, breast, and prostate tumors.
• Compares three distinct computational methods (MM/PBSA, ABFE, Umbrella Sampling) for a robust assessment.
• Includes structural contact analysis providing mechanistic insight into key stabilizing residues.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 1.1 MB file size suggests the dataset is relatively small in scale.

Provenance

Source

figshare

Collection Method

Computational assessment using molecular modeling and free energy calculations.

Freshness

Last updated 2026-04-18 13:11:35