The aim of this study was to investigate whether easily calculated and comprehended molecular surface properties can predict drug solubility and permeability with sufficient accuracy to allow theoretical absorption classification of drug molecules. For this purpose, structurally diverse, orally administered model drugs were selected from the World Health Organization (WHO)'s list of essential drugs. The solubility and permeability of the drugs were determined using well-established in vitro methods in highly accurate experimental settings. Descriptors for molecular surface area were generated from low-energy conformations obtained by conformational analysis using molecular mechanics calculations. Correlations between the calculated molecular surface area descriptors, on one hand, and solubility and permeability, on the other, were established with multivariate data analysis (partial least squares projection to latent structures (PLS)) using training and test sets. The obtained models were challenged with external test sets. Both solubility and permeability of the druglike molecules could be predicted with high accuracy from the calculated molecular surface properties alone. The established correlations were used to perform a theoretical biopharmaceutical classification of the WHO-listed drugs into six classes, resulting in a correct prediction for 87% of the essential drugs. An external test set consisting of Food and Drug Administration (FDA) standard compounds for biopharmaceutical classification was predicted with 77% accuracy. We conclude that PLS models of easily comprehended molecular surface properties can be used to rapidly provide absorption profiles of druglike molecules early on in drug discovery.