Computational ligand-based rational design: role of conformational sampling and force fields in model development

Abstract

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Targeting Drug Resistance in EGFR with Covalent Inhibitors: A Structure-Based Design Approach

“…[45][46][47] Moreover, empirical force field-based methods, for instance based on the "General Amber Force Field" (GAFF 48,49 ) are not accurate enough to discriminate possibly subtle population shifts arising from free energy differences below 1 kcal mol -1 . This represents the typical so-called "chemical accuracy" which poses a systematic limit to force field 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1...…”

Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing

“…2 In the field of computer-aided drug design, MM methods are well established in the context of both ligand-based and structure-based ligand optimization approaches. 3 An essential component of MM and MD studies, both for biomolecular systems and for drug-like molecules and other types of ligands, is the force field used in the calculations. Indeed, it is the force field – in combination with the sampling of the relevant conformations – that dictates the accuracy of the obtained results.…”

Computer-Aided Drug Design Methods

“…Figure 1 illustrates the basic CADD workflow that can be interactively used with experimental techniques to identify novel lead compounds as well as direct iterative ligand optimization (3, 4, 21, 22). The process starts with the biological identification of a putative target to which ligand binding should lead to antimicrobial activity.…”