To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

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SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Lead- and drug-like compounds: the rule-of-five revolution.

Privileged substructures are of potentially great importance in medicinal chemistry. These scaffolds are characterized by their ability to promiscuously bind to a multitude of receptors through a variety of favorable characteristics. This may include presentation of their substituents in a spatially defined manner and perhaps also the ability to directly bind to the receptor itself, as well as exhibiting promising characteristics to aid bioavailability of the overall molecule. It is believed that some privileged substructures achieve this through the mimicry of common protein surface elements that are responsible for binding, such as β- and gamma;-turns. As a result, these structures represent a promising means by which new lead compounds may be identified.

The combinatorial synthesis of bicyclic privileged structures or privileged substructures

The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.

Improved protein-ligand docking using GOLD.

The medicinal chemistry community has become increasingly aware of the value of tracking calculated physical properties such as molecular weight, topological polar surface area, rotatable bonds, and hydrogen bond donors and acceptors. We hypothesized that the shift to high-throughput synthetic practices over the past decade may be another factor that may predispose molecules to fail by steering discovery efforts toward achiral, aromatic compounds. We have proposed two simple and interpretable measures of the complexity of molecules prepared as potential drug candidates. The first is carbon bond saturation as defined by fraction sp3 (Fsp3) where Fsp3 = (number of sp3 hybridized carbons/total carbon count). The second is simply whether a chiral carbon exists in the molecule. We demonstrate that both complexity (as measured by Fsp3) and the presence of chiral centers correlate with success as compounds transition from discovery, through clinical testing, to drugs. In an attempt to explain these observations,...

Escape from flatland: increasing saturation as an approach to improving clinical success.

Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two cr...

Kenneth D. Kopple

Papers

Molecular properties that influence the oral bioavailability of drug candidates.