Field Interaction and Geometrical Overlap:  A New Simplex and Experimental Design Based Computational Procedure for Superposing Small Ligand Molecules

Abstract

Alignment of molecules is a crucial and time-consuming step in any 3D-QSAR study. For this reason, the field interaction and geometrical overlap (FIGO) procedure presented in this paper is particularly relevant because it can provide an objective and automatic superposition of ligands through the computation of an appropriate alignment index (AI). Ligand overlay takes place via a simplex optimization of the AI function. Experimental design strategies (full factorial design, D-optimal design) are used to define the starting positions of the superposing molecules. Overlay experiments are carried out to test the performance of the method. Comparison between the results obtained with FIGO and known ligand-receptor X-ray crystallographic data (Protein Data Bank) suggests that FIGO is an effective and reliable computational procedure.

Ultrafast shape recognition: Evaluating a new ligand-based virtual screening technology

“…the alignment problem have been proposed [4]; however important difficulties in this pursuit have been pointed out [5]. This paper focuses on molecular shape comparison as a way to carry out ligand-based Virtual Screening.…”

BRUTUS: Optimization of a grid-based similarity function for rigid-body molecular superposition. II. Description and characterization

“…Filled circles represent the grid points of energy field A, whereas open circles represent the grid points of energy field B. For example, grid point (1,1) is mapped to point (2,2) and grid point (2,1) is mapped to (4,2). If the grid separation of energy field B is increased, the precision of interpolation algorithm can improved, since the difference between the mapped grid points becomes shortened.…”

“…However, often there is no structural information available about the therapeutic target of interest [2][3][4], which limits the usefulness of receptor-based methods such as molecular docking [5]. In such situations, ligand-based methods are usually chosen.…”

SOMFA on Large Diverse Xenoestrogen Dataset: The Effect of Superposition Algorithms and External Regression Tools

“…This has led to the development of 3D-QSAR methods such as Comparative Molecular field Analysis (CoMFA) [15] and Grid-Independent Descriptors (GRIND) [16]. Although the 3D-QSAR techniques are generally considered to be the most effective means of predicting the biological activity, they usually require an accurate superposition of structures, which has proven to be the major bottleneck [17,18]. The alignment procedure often requires considerable human intervention and it is generally the most time-consuming phase of the 3D-QSAR analysis [16].…”