Computational Dataset for Polymer–Pharmaceutical Interactions: MD/MM-PBSA and DFT Resources for Molecularly Imprinted Polymer (MIP) Design

Molecularly imprinted polymers (MIPs) are promising sorbents for selectively capturing pharmaceutically active compounds (PhACs), but design remains slow because candidate screening is largely experimental or based on computationally expensive methods. We present MIP–PhAC, an open, curated resource of polymer–pharmaceutical interaction energies generated from molecular dynamics (MD) followed by MM/PBSA analysis, with a small DFT subset for cross-method comparison. This resource is comprised of two complementary datasets: MIP–PhAC-Calibrated, a benchmark set with manually verified pH-7 microstates that reports both monomeric (pre-polymerized) and polymeric (short-chain) MD/MMPBSA energies and includes a DFT subset; and MIP–PhAC-Screen, a broader, high-throughput collection produced under a uniform automated workflow (including automated protonation) for rapid within-polymer ranking and machine learning development. For each MIP—PhAC pair we provide Δ G * components (electrostatics, van der Waals, polar and non-polar solvation; −TΔS omitted), summary statistics from post-convergence frames, simulation inputs, and chemical metadata. To our knowledge, MIP–PhAC is the largest open, curated dataset of polymer–pharmaceutical interaction energies to date. It enables benchmarking of end-point methods, reproducible protocol evaluation, data-driven ranking of polymer–pharmaceutical combinations, and training/validation of machine learning (ML) models for MIP design on modest compute budgets.