Pharmacophore Generation for Multiple Ligands

An Improved Receptor-Based Pharmacophore Generation Algorithm Guided by Atomic Chemical Characteristics and Hybridization Types

Pharmacophore-based virtual screening is an important and leading compound discovery method. However, current pharmacophore generation algorithms suffer from difficulties, such as ligand-dependent computation and massive extractive chemical features. On the basis of the features extracted by the five probes in Pocket v.3, this paper presents an improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. The algorithm works under the constraint of receptor atom hybridization types and space distance. Four chemical characteristics (H-A, H-D, and positive and negative charges) were extracted using the hybridization type of receptor atoms, and the feature point sets were merged with 3 Å space constraints. Furthermore, on the basis of the original extraction of hydrophobic characteristics, extraction of aromatic ring chemical characteristics was achieved by counting the number of aromatics, searching for residual base aromatic ring, and determining the direction of aromatic rings. Accordingly, extraction of six kinds of chemical characteristics of the pharmacophore was achieved. In view of the pharmacophore characteristics, our algorithm was compared with the existing LigandScout algorithm. The results demonstrate that the pharmacophore possessing six chemical characteristics can be characterized using our algorithm, which features fewer pharmacophore characteristics and is ligand independent. The computation of many instances from the directory of useful decoy dataset show that the active molecules and decoy molecules can be effectively differentiated through the presented method in this paper.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.