Utilising 'beyond rule of five' chemical space is becoming increasingly important in drug design, but is usually at odds with good oral absorption. The formation of intramolecular hydrogen bonds in drug molecules is hypothesised to shield polarity facilitating improved membrane permeability and intestinal absorption. NMR based evidence for intramolecular hydrogen bonding in several 'beyond rule of five' oral drugs is described. Furthermore, the propensity for these drugs to form intramolecular hydrogen bonds could be predicted for through modelling the lowest energy conformation in the gas phase. The modulation of apparent lipophilicity through intramolecular hydrogen bonding in these molecules is supported by intrinsic cell permeability and intestinal absorption data in rat and human.
This paper evaluates the effectiveness of various similarity coefficients for 2D similarity searching when multiple bioactive target structures are available. Similarity searches using several different activity classes within the MDL Drug Data Report and the Dictionary of Natural Products databases are performed using BCI 2D fingerprints. Using data fusion techniques to combine the resulting nearest neighbor lists we obtain group recall results which, in many cases, are a considerable improvement on standard average recall values obtained for individual structures. It is shown that the degree of improvement can be related to the structural diversity of the activity class that is searched for, the best results being found for the most diverse groups. The group recall of active compounds using subsets of the class is also investigated: for highly self-similar activity classes, the group recall improvement saturates well before the full activity class size is reached. A rough correlation is found between the relative improvement using the group recall and the square of the number of unique compounds available in all of the merged lists. The Tanimoto coefficient is found unambiguously to be the best coefficient to use for the recovery of active compounds using multiple targets. Furthermore, when using the Tanimoto coefficient, the "MAX" fusion rule is found to be more effective than the "SUM" rule for the combination of similarity searches from multiple targets. The use of group recall can lead to improved enrichment in database searches and virtual screening.
We have developed BLEEP (biomolecular ligand energy evaluation protocol), an atomic level potential of mean force (PMF) describing protein–ligand interactions. Here, we present four tests designed to assess different attributes of BLEEP. Calculating the energy of a small hydrogen‐bonded complex allows us to compare BLEEP's description of this system with a quantum‐chemical description. The results suggest that BLEEP gives an adequate description of hydrogen bonding. A study of the relative energies of various heparin binding geometries for human basic fibroblast growth factor (bFGF) demonstrates that BLEEP performs excellently in identifying low‐energy binding modes from decoy conformations for a given protein–ligand complex. We also calculate binding energies for a set of 90 protein–ligand complexes, obtaining a correlation coefficient of 0.74 when compared with experiment. This shows that BLEEP can perform well in the difficult area of ranking the interaction energies of diverse complexes. We also study a set of nine serine proteinase–inhibitor complexes; BLEEP's good performance here illustrates its ability to determine the relative energies of a series of similar complexes. We find that a protocol for incorporating solvation does not improve correlation with experiment. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 1177–1185, 1999
Historically the early stages of drug discovery have been based on finding the highest affinity compounds that bind to the target of interest, with little consideration for the forces driving the binding event. The association constant (K a ) can be defined by the equation DG = ÀRTln K a , with DG = DHÀTDS. To fully describe K a it would therefore be beneficial to characterize both of the thermodynamic terms (DH and DS) that drive this affinity for binding. The importance of separating affinity into its thermodynamic components is emphasized by the ubiquitous "enthalpy/entropy compensation effect", where large changes in DH and DS tend to be of similar but opposite signs and there is no net change in affinity, despite potentially very different binding mode.[1] It has been proposed by Freire, [2] and Ward & Holdgate [3] that it is advantageous, in terms of both potency and selectivity, to start from an enthalpically-driven lead. It can also be argued that choosing compounds with different binding modes increases the variety of chemical substrate for optimization, therefore reducing the risk of all the compounds encountering the same side effects. These points emphasize the need to measure thermodynamic signatures of lead compounds as early in the drug discovery process as possible.The only method that directly measures the thermodynamics of a binding event in solution is isothermal titration calorimetry (ITC).[4] Even though ITC can give a full thermodynamic signature (DG obs , DH obs , DS obs and K B, obs ) from a single experiment, the full utilization of the technique for lead optimization has been hampered by technical limitations requiring substantial quantities of reagents. In addition, data have frequently been collected from optimized, but varied, experimental conditions for a particular system, and without appropriate controls the interpretation of results between studies is difficult. Here, we demonstrate that with recent advances in ITC technology [5] and comparing subtly modified ligands against the same target, under identical conditions, and with X-ray data support, thermodynamic measurements can provide medicinal chemists with another differentiator in their quest to discover the best lead compounds. Moreover, these data are informative to medicinal chemists as they are applicable to situations where a less complete biophysical analysis is possible.We chose human carbonic anhydrase (hCA II) as a favorable system for this investigation as there is already a wealth of both 3D structures and calorimetric data available, which has established this protein as the leading model system. [6][7][8][9][10][11] Additionally, the protein binds benzene sulfonamides (BSAs) with a 1:1 stoichiometry and does not undergo gross conformational changes upon binding, providing an essentially thermodynamically closed system that will therefore not complicate interpretation of the binding thermodynamics. The binding of BSA to hCA II is driven mainly through four H bonds from the sulfonamide, two H bonds to the Zn co-...
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