A marriage made in torsional space: using GALAHAD models to drive pharmacophore multiplet searches

Abstract

Pharmacophore multiplets are useful tools for 3D database searching, with the queries used ordinarily being derived from ensembles of random conformations of active ligands. It seems reasonable to expect that their usefulness can be augmented by instead using queries derived from single ligand conformations obtained from aligned ligands. Comparisons of pharmacophore multiplet searching using random conformations with multiplet searching using single conformations derived from GALAHAD (a genetic algorithm with linear assignment for hypermolecular alignment of datasets) models do indeed show that, while query hypotheses based on random conformations are quite effective, hypotheses based on aligned conformations do a better job of discriminating between active and inactive compounds. In particular, the hypothesis created from a neuraminidase inhibitor model was more similar to half of 18 known actives than all but 0.2% of the compounds in a structurally diverse subset of the World Drug Index. Similarly, a model developed from five angiotensin II antagonists yielded hypotheses that placed 65 known antagonists within the top 0.1-1% of decoy databases. The differences in discriminating power ranged from 2 to 20-fold, depending on the protein target and the type of pharmacophore multiplet used.

Antiproliferative Agents That Interfere with the Cell Cycle at the G1→S Transition: Further Development and Characterization of a Small Library of Stilbene‐Derived Compounds

“…GALAHAD aligns molecules and generates pharmacophore hypotheses in the form of hypermolecules incorporating the structural information of the dataset. [32,33] The core computational methodology of GALAHAD is a genetic algorithm that operates on a set of individual models in which each model is defined by a set of torsions for each molecule in the dataset. The pharmacophore is obtained through a procedure whereby the program first identifies corresponding features in ligands paired on the basis of structural similarity, then aligns the conformations in Cartesian space and merges the ligands into a single hypermolecule.…”

Pharmacophore modeling, virtual screening, and 3D-QSAR studies on a series of non-steroidal aromatase inhibitors

“…The feature considered in developing the pharmacophore model includes HBD atoms, HBA atoms, and hydrophobic and charged centers (Richmond et al, 2006;Shepphird and Clark, 2006;Andrade et al, 2008). In our study, twelve compounds shown in Table 1 were selected to carry out the pharmacophore hypothesis, and the genetic algorithm was used to create conformers for all molecules.…”

Computational Strategies in Cancer Drug Discovery

“…For this study, they aimed to develop an LB model based on previous work in an effort to discover new classes of potential c-Myc-Max inhibitors. The authors made use of the Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) in SYBYL 8.0 software (Shepphird and Clark 2006) to develop a ligandbased pharmacophore model that takes into account ligand flexibility, steric overlaps, and strain energies. Model generation was based on a set of six c-Myc-Max inhibitors that these authors had previously reported -composed of the original compound, which they called 10058-F4, and five compounds derived from it -and gave 20 pharmacophore model hypotheses.…”

“…This was accomplished using the Tuplets function in SYBYL 8.0 (Shepphird and Clark 2006) software. The refined LB model was then tested using a set of ten compounds composed of four inactive analogues of the original compound 10058-F4 and the six compounds described above.…”