Number of rotatable bonds

Veber and coauthors have analyzed more than 1000 drug candidates and have shown that compounds that have fewer rotatable bonds and tPSA scores less than 140 A  

The ability of a molecule to bind to the potassium channel, hERG (human ether-a-go-go related gene), is a serious pharmacological concern and may lead to the failure to progress an otherwise active and druglike molecule during the lead optimization process. Cardiac QT interval prolongation has been associated to some extent with 

Chemoinformatic Analysis of the Predicted Hansch Substituent Constants of the Diversity Reagents for Design of Vector Exploration Libraries 

The chemical diversity represented in this way is then more easily interpreted when hits are detected from libraries designed in this manner. 

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As the number of conformations increases exponentially with the number of rotatable bonds, for most molecules it is not feasible to take all possible conformations into account. However, a balanced sampling of the conformational space should be ensured if only subsets arc being considered. In order to restrict the number of geometries output, while retaining a maximum of conformational diversity, ROTATE offers the possibility of classifying the remaining conformations, i.c., similar conformations can be combined into classes. The classification is based on the RMS deviation between the conformations, either in Cartesian (RMS y 7if [A]) or torsion space in [ ], The RMS threshold, which decides whether two... 

Prediction of various physicochemical properties such as solubihty, lipophhicity log P, pfQ, number of H-donor and acceptor atoms, number of rotatable bonds, polar surface area), drug-likeness, lead-likeness, and pharmacokinetic properties (ADMET profile). These properties can be applied as a filter in the prescreening step in virtual screening. 

At Novartis, so-called BioavailabiUty Radar Plots [44] are used to visually display the oral absorption potential of molecules. On these plots five important calculated descriptors (log P, molecular weight, PSA, number of rotatable bonds and water solubility score [45]) are displayed on the axes of a pentagonal radar plot and compared with predefined property limits (green area) which were determined by the analysis of marketed oral drugs. These plots provide an intuitive tool that displays multiple parameters as a single chart in a straightforward but informative way, providing visual feedback about the molecule s bioavailabiUty potential (Fig. 5.5). 

Systematic searches exhaustively sample conformational space by sequentially incrementing the torsional angles of aU of the rotatable bonds in a given molecule. This conceptually simple approach is straightforward to implement, but scales exponentially with respect to the number of rotatable bonds. To control the exponential increase in the number of potential conformers obtained, systematic searches are usually combined with tree-based search techniques taken from computer science. Even the best implementations of systematic searches become impractical beyond several rotatable bonds (typically greater than 10). Stochastic searches are based on probabiHstic theories and are better suited to calculations... 

Fig. 8.7 The average number of conformations generated as a function of the number of rotatable bonds for compounds in the GVK Bio database.

In addition to the in vitro assays described above, physical properties should be calculated for all new compounds designed for synthesis. It is necessary to keep in mind the target values for leads, such as MW < 450, clogP < 4.0, and PSA < 80. It has been demonstrated that properties such as MW and clogP increase during optimization [26], so that a lead needs to have lower values for these properties than a drug candidate. Additional factors that make up the rule-of-five [27] as well as the number of rotatable bonds as described by Veber [28] can also be tracked. While... 

PPII helix-forming propensities have been measured by Kelly et al. (2001) and A. L. Rucker, M. N. Campbell, and T. P. Creamer (unpublished results). In the simulations the peptide backbone was constrained to be in the PPII conformation, defined as (0,VO = ( — 75 25°, +145 25°), using constraint potentials described previously (Yun and Hermans, 1991 Creamer and Rose, 1994). The AMBER/ OPLS potential (Jorgensen and Tirado-Rives, 1988 Jorgensen and Severance, 1990) was employed at a temperature of 298° K, with solvent treated as a dielectric continuum of s = 78. After an initial equilibration period of 1 x 104 cycles, simulations were run for 2 x 106 cycles. Each cycle consisted of a number of attempted rotations about dihedrals equal to the total number of rotatable bonds in the peptide. Conformations were saved for analysis every 100 cycles. Solvent-accessible surface areas were calculated using the method of Richmond (1984) and a probe of 1.40 A radius. 

Oral bioavailability of a drug is primarily dependent upon its rate and extent of drug absorption and systemic clearance. Systemic clearance is primarily composed of hepatic, renal and biliary clearance. The PK properties are in turn directly impacted by the drug s physical properties, such as, log P, log D and pKa. The physical properties are in turn a function of the compound s structure, molecular weight, number of hydrogen bond donors and acceptors, and number of rotatable bonds. Oral bioavailability is the outcome from the dynamic interplay of these factors in the biological system. 

However, the CIMI/bc descriptors are relatively simplistic as they did not recognize differences between some important atom types (e.g., halogens). Based on the results of ARD approach, the octanol-water partition coefficient (log P), the molecular flexibility (number of rotatable bonds), the PSA, and the number of hydrogen bond donors were the most important descriptors in the model. None of the descriptors were significantly correlated with each other except log P and PSA for which the correlation was not high ( 0.5). The molecular weight was found to be the least significant descriptor in the model. 

Molecular weight is the most significant parameter for predicting the BBB permeability, followed byTPSA, log I , number of hydrogen-bond acceptors, number of hydrogen-bond donors, P-gp substrate probability, and finally the number of rotatable bonds. 

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