1724 J. Chem. Inf. Model. 2010, 50, 1724–1735 Training a Scoring Function for the Alignment of Small Molecules Shek Ling Chan* and Paul Labute Chemical Computing Group, Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, H3A 2R7, Canada Received June 11, 2010 A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods. INTRODUCTION In drug discovery projects, the three-dimensional (3D) structure of the receptor is not always available. In such cases, ligand optimization often depends on a meaningful alignment of the active compounds. Pharmacophore model building, 3D quantitative structure-activity relationship (QSAR), comparative molecular ﬁeld analysis (CoMFA),1 and ligand-based virtual screening all depend on a good algorithm to ﬂexibly align small molecules. In light of the importance of the subject, many methods have been devel- oped to perform the task. On top of the methods mentioned in the comprehensive review of Lemmen and Lengauer,2 new efforts have continued to appear.3-16 Similar to docking, the small molecule alignment problem can be divided into two parts. First, the conformational/pose space has to be thoroughly searched. Second, a scoring function needs to be able to distinguish good alignment poses from other possibilities. The current work is about obtaining a good scoring function for this purpose. In principle, a scoring function that can distinguish the correct alignment pose from incorrect ones is not necessarily the same as a scoring function that upon minimization gives poses that resemble the correct one as much as possible. In practice, a good scoring function probably performs well for both purposes. In this work, we are primarily interested in a scoring function for distinguishing good poses. In any case, we believe that the exact answer to the molecular alignment problem may not be simple. It depends on how the crystal structures are superposed. For example, using only R carbons in the binding pocket for superposition may give a slightly different answer from that using all the pocket atoms or using the pharmacophore elements involving the ligand and the pocket. And then there is the resolution of the crystal structure itself. Moreover, the ligand may have some freedom of movement inside a pocket. Hence trying to nudge down the geometric difference between a proposed alignment hypothesis and the “correct” alignment all the way to zero may not be necessary, or even meaningful, as long as the correct alignment mode is obtained. * Corresponding author. E-mail: slchan3@yahoo.com. For scoring functions used in docking, one can use molecular mechanics force ﬁelds derived from ﬁrst principles. The same approach is not feasible for scoring functions for small molecule alignment. Such functions need to be derived from some statistical analysis of a training set of known molecular alignments. The Protein Data Bank17 (PDB) is an obvious source to obtain such a training set. As technology progresses, the number of entries in the PDB has grown exponentially. Just in the ﬁve years since 2003, the number of entries has more than doubled (see http://www.pdb.org). Moreover, many old entries have been revisited and cleaned up, and many low-resolution structures have been superseded by better ones. By now, there are hundreds of systems of entries with identical proteins but different ligands. We believe that now is a good time to make use of the growth in the PDB to revisit the derivation of a scoring function for molecular alignment. METHODS Scoring Function. For the problem of small molecule alignment, there are two main types of scoring functions. The ﬁrst type is atom based: When two molecules are being aligned, the score consists of a sum of terms that are based on intermolecular atom pairs (i.e., each pair has one atom coming from each molecule). The second type of score is ﬁeld based: The electrostatic or steric ﬁelds of the molecules or their surfaces are compared to arrive at a score. This would be somewhat slower to compute since the ﬁelds or the molecular surfaces need to be calculated from the atomic coordinates. Hence it would be difﬁcult to ﬂexibly reﬁne an ensemble of aligned molecules or to deal with a huge number of conformers. Indeed several of the ﬁeld-based alignment methods rely on an independent conformational search engine to generate good conformers (e.g., Shapelets,6 BRUTUS,13 the “Molecular Field Extrema” method,11 and MIMIC18). Since our scoring function will be used to distinguish good binding poses among a huge number of possibilities, it needs to be calculated quickly. An atom-based function would hence be more appealing than a molecular ﬁeld-based function. 10.1021/ci100227h  2010 American Chemical Society Published on Web 09/10/2010