Metal–organic frameworks (MOFs) are porous materials with exceptional host–guest properties with huge potential for gas separation. The combinatorial design of MOFs demands the in silico screening of the nearly infinite combinations of structural building blocks using efficient computational tools. We report here a novel atomic property weighted radial distribution function (AP-RDF) descriptor tailored for large-scale Quantitative Structure–Property Relationship (QSPR) predictions of gas adsorption of MOFs. A total of ∼58,000 hypothetical MOF structures were used to calibrate correlation models of the methane, N2, and CO2 uptake capacities from grand-canonical Monte Carlo (GCMC) simulations. The principal component analysis (PCA) transform of the AP-RDF descriptors exhibited good discrimination of MOF inorganic SBUs, geometrical properties, and more surprisingly gas uptake capacities. While the simulated uptake capacities correlated poorly to the void fraction, surface area, and pore size, the newly introduced AP-RDF scores yielded outstanding QSPR predictions for an external test set of ∼25,000 MOFs with R 2 values in the range from 0.70 to 0.82. The accuracy of the predictions decreased at low pressures, mainly for MOFs with V2O2 or Zr6O8 inorganic structural building units (SBUs) and organic SBUs with fluorine substituents. The QSPR models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials. The predictive models of the gas uptake capacities of MOFs are available online via our MOF informatics analysis (MOFIA) tool.