Molecular Shape Technologies in Drug Discovery: Methods and Applications

Abstract

Shape complementarity is a critically important factor in molecular recognition among drugs and their biological receptors. The notion that molecules with similar 3D shapes tend to have similar biological activity has been recognized and implemented in computational drug discovery tools for decades. But the low computational efficiency and the lack of widely accessible software tools limited the use of early shape-matching algorithms. However, recent development of fast and accurate shape comparison tools has changed the landscape, and facilitated the wide spread use of both the ligand-based and receptor-based shape-matching technologies in drug discovery. In this article, we summarize some of the well-known shape algorithms. We first describe the computational principles for both the superposition-based and the superposition-free shape-matching methods. These include ROCS (Rapid Overlay of Compound Structures), SQ, and the CatShape method in the former category; and the shape signatures algorithm and USR (Ultrafast Shape Recognition) that belong to the latter category. We then highlight some recent validation studies and practical applications of various shape technologies. Because of the rapid development of modern shape-matching algorithms, and the increasingly affordable computational resources and software tools, we anticipate much broader use of the molecular shape technologies in future drug discovery. They will be especially useful in chemogenomics research, where large scale associations between small molecules and protein targets are studied. Thus, molecular shape technologies, together with well-defined pharmacophore constraints, can afford both efficient and effective means for drug discovery and chemical genomics research.

Plane of Best Fit: A Novel Method to Characterize the Three-Dimensionality of Molecules

“…4 Compound shape has long been recognized as an important factor in molecular recognition between a ligand and its biological targets, and the optimal spatial orientation of pharmacophoric features is essential for ligand efficient binding of small molecules. 5−7 Natural products frequently incorporate scaffolds with significant three-dimensional (3D) character 8 and recent important breakthroughs in the discovery of protein–protein interaction inhibitors often involve scaffolds incorporating 3D character; 9 for example, inhibitors of the bromodomain BRD4/chromatin interaction, 10 the LEDGF/p75 integrase interaction, 11 the Bcl2 family proteins, 12 and of the MDM2/p53 interaction (Figure 1). 13 For each ligand shown in Figure 1, a conformation with pronounced 3D shape has been captured in the respective protein binding site by X-ray crystallography.…”

Hierarchical virtual screening approaches in small molecule drug discovery

“…Similarity based techniques include methods accessing the similarity of one or a few experimentally identified hits with molecules in a large library in terms of their physicochemical properties [33], structural fingerprints [34], 3D-shape [35], electrostatic potential [36] and pharmacophore features [37] etc. Similarity based techniques include methods accessing the similarity of one or a few experimentally identified hits with molecules in a large library in terms of their physicochemical properties [33], structural fingerprints [34], 3D-shape [35], electrostatic potential [36] and pharmacophore features [37] etc.…”

A new perspective of shape recognition to discover the phase transition of finite‐size clusters

“…To discriminate between geometries, the shape‐recognition technique has been widely used for virtually screening protein molecule databases in drug design and discovery; however, this strategy is seldom applied to analyze small molecular systems, such as metallic clusters, whose molecular geometries are thermally perturbed. The idea of applying the shape‐recognition technique to elucidate the structural evolution of cluster melting originated from our observations regarding the fundamental differences in the dynamics of atoms or molecules between finite (e.g., cluster) and bulk infinite systems.…”