Understanding chemical interactions between organic and metal-organic molecules has wide-ranging interest to the vapor deposition community for creating hybrid organic-inorganic materials via techniques such as molecular layer deposition and vapor phase infiltration (VPI). In the case of VPI, a vapor-phase metal-organic precursor is infused into the bulk of a polymer and becomes incorporated at the nanoscale through either chemical interaction with the polymer or the formation of a non-volatile species via the introduction of a co-reactant. VPI has applicability in a number of industrially relevant fields including the creation of novel organic-inorganic hybrid membranes which have shown enhanced stability in organic solvents, while retaining high permeance and selectivity. Motivated by this application, this work uses density functional theory (DFT) to explore chemical interactions occurring during the VPI of polymer of intrinsic microporosity (PIM-1, a polymeric membrane material) with trimethylaluminum (TMA) and its co-reaction with water. These computations revealed that the coordination between the polymer and metal-organic is a critical mechanism for the formation of the hybrid and its resultant solvent stability. To expand understanding of this critical characteristic and accelerate the design of organic-inorganic hybrid materials, a DFT dataset of computed binding energies was generated from suitable and representative atomic-level models of several common polymer functional groups and over 100 metal-organic precursors. From this dataset, a predictive machine learning model for the binding energy of metal-organic molecules to polymers has been developed. This predictive model, along with the chemical guidelines obtained from feature analysis, will aid the selection of potential candidates for novel organic-inorganic hybrid membranes as well as hybrid material creation as a whole.