Approximately half of gene lesions responsible for human inherited diseases are due to an amino acid substitution, showing that this mutational mechanism plays a large role in diseases. Distinguishing neutral sequence variations from those responsible for the phenotype is of major interest in human genetics. Because in vitro validation of mutations is not always possible in diagnostic settings, indirect arguments must be accumulated to define whether a missense variation is causative. To further differentiate neutral variants from pathogenic nucleotide substitutions, we developed a new tool, UMD-Predictor. This tool provides a combinatorial approach that associates the following data: localization within the protein, conservation, biochemical properties of the mutant and wild-type residues, and the potential impact of the variation on mRNA. To evaluate this new tool, we compared it to the SIFT, PolyPhen, and SNAP software, the BLOSUM62 and Yu's Biochemical Matrices. All tools were evaluated using variations from well-validated datasets extracted from four UMD-LSDB databases (UMD-FBN1, UMD-FBN2, UMD-TGFBR1, and UMD-TGFBR2) that contain all published mutations of the corresponding genes, that is, 1,945 mutations, among which 796 different substitutions corresponding to missense mutations. Our results show that the UMD-Predictor algorithm is the most efficient tool to predict pathogenic mutations in this context with a positive predictive value of 99.4%, a sensitivity of 95.4%, and a specificity of 92.2%. It can thus enhance the interpretation of variations in these genes, and could easily be applied to any other disease gene through the freely available UMD generic software (http://www.umd.be).