1734 J. Chem. Inf. Model., Vol. 50, No. 9, 2010 superpose the two rings on either end but difﬁcult to distinguish between the two binding modes. Hence all the cross-alignments within a binding mode were obtained correctly, but the inverse binding mode was obtained for each pair across the different binding modes. Scoring Function Training with All Data. When the scoring function was trained for the validation runs, systems involved in the test sets were excluded. After the validation runs, the training process was repeated to make use of all data, including systems involved in the test sets. It turned out that using all the data did not lead to any change in the scoring function. The updated Tables 2a-2h are given in Tables a-h of section B of the Supporting Information. Although the numbers of good poses among the top 25 (n25) have changed slightly, the positions for the optimal values for all the parameters have not. Hence the scoring function remained the same when all data was included for training. CONCLUSION Based on the PDB17 and SCOP,28 a comprehensive set of aligned ligands binding in the same pocket has been compiled. We used only high-resolution structures and considered only pockets that are highly similar and have diversiﬁed, drug-like ligands. For each pair of ligands binding in the same pocket, one ligand was used as the target, and dummy aligned poses of the other ligand were generated. By studying the correlation between the score of these dummy poses and their similarities with the correct answer, a small molecule alignment scoring function was built up term by term. Eventually we obtained the scoring function composed of terms given by Table 1, except terms T6 and T7, with parameters as given by Table 3. We veriﬁed our scoring function by comparing results of rigid-body and ﬂexible alignments performed using the ﬂexible alignment functionality in MOE22 with those reported in the literature. We found that our results are superior to those of ﬁve methods reported in two recent publications.6,23 We are happy to share our comprehensive aligned small molecules data set with other scientists so that they can use it to optimize their own scoring functions. ACKNOWLEDGMENT We thank our colleagues in Chemical Computing Group for valuable comments. Supporting Information Available: In the main text above, parameters of the scoring function were trained by considering the “top 25 scoring poses” (Results and Discus- sion Section) and by using data excluding the validation test sets. 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