Pharmacophore identification

In a recent review the pharmacophore identification programs Catalyst, DISCO, and GASP have been compared [83]. 

Chen X, Rusinko A, Tropsha A,Young SS. Automated pharmacophore identification for large chemical data sets. J Chem Inf Comput Sci 1999 39(5) 887-96. 

The antitumor activities of JV-substituted 11 -oxo-11 //-pyrido[2,l -b]-quinazoline-6-carboxamides were studied (88JMC707 92MI3 95JMC2418). Using an automated pharmacophore identification procedure, 11-oxo-11//-pyrido[2,l-h]quinazoline-6-carboxamide (407) was predicted to be an active inhibitor of human immunodeficiency virus type 1 (HIV-1) integrase (97JMC920). 

Taken together, the examples shown above illustrate typically some pre-computer attempts to elucidate pharmacophoric patterns usable as guides for the design of new drugs. They prepared the minds for Garland Marshall s seminal publications (see references in [31, 32]) on computer-aided pharmacophore identification and all the derived applications that will be presented in the following chapters. 

Wermuth, C. G., Langer, T., Pharmacophore identification. In 3D QSAR in Drug Design. Theory Methods and Applications, Kubinyi, H. (ed.). ESCOM, Leiden, 1993, pp. 117-136. 

At the beginning of this chapter we will look into the different automated alignment methods as correct alignment is the first and most important prerequisite for a successful pharmacophore identification process. Further, we will elaborate how essential issues of pharmacophore modeling such as conformational search, pharmacophore feature definitions, compounds structure storage and screening are handled by various available software packages. 

Even though the original authors of DISCO do not consider it to be an automated pharmacophore identification program [53], we decided to include the method in this review because of its considerable influence over the development of modern pharmacophore modeling tools. 

By design, no conformational engine was implemented in DISCO, based on the assumption that at the time, no universal force fields and methods suitable for all types of compounds were available [53]. However, the commercial distributor Tripos provides access to 3D converters and conformational search engines such as Concord and Confort via the Sybyl interface. These algorithms will not be reviewed here as strictly seen they are not part of any pharmacophore identification program. The distance geometry approach has been used... 

Unlike other pharmacophore identification routines, the conformational search is performed on-the-fly in GASP and represents an integral part of the program. Each compound is input a single, low-energy conformation and random rotations and translations are applied in order to explore the conformational variation prior to superposition. 

The pharmacophore identification process as implemented in the Catalyst package involves 3D structure generation, followed by conformational search and definition of the pharmacophore points consistent with the training set. 

SCAMPI (Statistical Classification of Activities of Molecules for Pharmacophore Identification) is a program developed in C language by Chen et al. [104]. According to the authors, it allows the use of datasets of approximately 1000-2000 compounds. The SCAMPI program s implementation has been done to allow users to visualize the molecules and the generated pharmacophores in the Sybyl environment. 

As opposed to other pharmacophore generation methods that treat the confor-mer expansion and pharmacophore identification phases separately, SCAMPI combines the two searches and lets them depend on each other. Figure 2.2 illustrates the workflow used by SCAMPI. 

Finn, P.W., Karraki, L. E., Latombe, J.-C., Motwani, R., Shelton, C., Venkatasubra-manian, S., Yao, A., RAPID randomized pharmacophore identification for drug design, in Proc of the 3rd annual symposium on computational geometry, Nice, France, ACM Press, p. 324-333. 

Bersuker, I.B., Bahceci, S., Boggs, J.E. Improved electron-conformational method of pharmacophore identification and bioactivity prediction. Application to angiotensin converting enzyme inhibitors. Journal of Chemical Information and Computer Science 2000, 40, 1363-1376. 

E. M. Krovat, T. Langer. Non-peptide angiotensin II receptor antagonists chemical feature based pharmacophore identification. /. Med. Chem., 46, 716-726, 2003. 

Richmond NJ, Abrams CA, Wolohan PR et al (2006) GALAHAD 1. pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20 567-587... 

Golender VE, Vorpogel ER (1993) Computer Assisted-Pharmacophore Identification. In Kubinyi H (ed) 3D-QSAR in Drug Design Theory, Methods and Application. ESCOM Science Publishers, Leiden, Netherlands, p 137... 

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