The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.

The maximum achievable accuracy of in silico models depends on the quality of the experimental data. Consequently, experimental uncertainty defines a natural upper limit to the predictive performance possible. Models that yield errors smaller than the experimental uncertainty are necessarily overtrained. A reliable estimate of the experimental uncertainty is therefore of high importance to all originators and users of in silico models. The data deposited in ChEMBL was analyzed for reproducibility, i.e., the experimental uncertainty of independent measurements. Careful filtering of the data was required because ChEMBL contains unit-transcription errors, undifferentiated stereoisomers, and repeated citations of single measurements (90% of all pairs). The experimental uncertainty is estimated to yield a mean error of 0.44 pK(i) units, a standard deviation of 0.54 pK(i) units, and a median error of 0.34 pK(i) units. The maximum possible squared Pearson correlation coefficient (R(2)) on large data sets is estimated to be 0.81.

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A powerful screening by NMR methodology (WaterLOGSY), based on transfer of magnetization from bulk water, for the identification of compounds that interact with target biomolecules (proteins, RNA and DNA fragments) is described. The method exploits efficiently the large reservoir of H2O magnetization. The high sensitivity of the technique reduces the amount of biomolecule and ligands needed for the screening, which constitutes an important requirement for high throughput screening by NMR of large libraries of compounds. Application of the method to a compound mixture against the cyclin-dependent kinase 2 (cdk2) protein is presented.

Hydrogen-bonding interactions play an important role in many chemical and biological systems. Fluorine acting as a hydrogen-bond acceptor in intermolecular and intramolecular interactions has been the subject of many controversial discussions and there are different opinions about it. Recently, we have proposed a correlation between the propensity of fluorine to be involved in hydrogen bonds and its (19)F NMR chemical shift. We now provide additional experimental and computational evidence for this correlation. The strength of hydrogen-bond complexes involving the fluorine moieties CH2F, CHF2, and CF3 was measured and characterized in simple systems by using established and novel NMR methods and compared to the known hydrogen-bond complex formed between acetophenone and p-fluorophenol. Implications of these results for (19)F NMR screening are analyzed in detail. Computed values of the molecular electrostatic potential at the different fluorine atoms and the analysis of the electron density topology at bond critical points correlate well with the NMR results.

A novel strategy for the design of a fluorinated fragment library that takes into account the local environment of fluorine is described. The procedure, based on a fluorine fingerprints descriptor, and the criteria used in the design, selection, and construction of the library are presented. The library, named LEF (Local Environment of Fluorine), combined with (19)F NMR ligand-based screening experiments represents an efficient and sensitive approach for the initial fragment identification within a fragment-based drug discovery project and for probing the presence of fluorophilic protein environments. Proper setup of the method, according to described theoretical simulations, allows the detection of very weak-affinity ligands and the detection of multiple ligands present within the same tested mixture, thus capturing all the potential fragments interacting with the receptor. These NMR hits are then used in the FAXS experiments for the fragment optimization process and for the follow-up screening aimed at identifying other chemical scaffolds relevant for the binding to the receptor.

Abnormal proliferation mediated by disruption of the normal cell cycle mechanisms is a hallmark of virtually all cancer cells. Compounds targeting complexes between cyclin-dependent kinases (CDK) and cyclins, such as CDK2/cyclin A and CDK2/cyclin E, and inhibiting their kinase activity are regarded as promising antitumor agents to complement the existing therapies. From a high-throughput screening effort, we identified a new class of CDK2/cyclin A/E inhibitors. The hit-to-lead expansion of this class is described. X-ray crystallographic data of early compounds in this series, as well as in vitro testing funneled for rapidly achieving in vivo efficacy, led to a nanomolar inhibitor of CDK2/cyclin A (N-(5-cyclopropyl-1H-pyrazol-3-yl)-2-(2-naphthyl)acetamide (41), PNU-292137, IC50 = 37 nM) with in vivo antitumor activity (TGI > 50%) in a mouse xenograft model at a dose devoid of toxic effects.

Ligand-based fluorine NMR screening has gained popularity in drug discovery projects during the past decade and has become a powerful methodology to produce high quality hits. Its high sensitivity to protein binding makes it particularly suitable for fragment screening, allowing detection and binding strength measurement of very weak affinity ligands. The screening can be performed in direct or competition format, and its versatility allows application to complex biological and chemical systems. As the potential of the methodology has now been recognized and successfully demonstrated in several relevant medicinal chemistry projects, it is now an appropriate time to report the learned lessons and point the way to the future. In this Perspective the principles of the methodology along with several applications to pharmaceutical projects are presented.