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This paper presents a new protocol based on 3D molecular descriptors using QM calculations for use in CoMFA-like 3D-QSSR. The new method was developed and then applied to predict catalytic selectivity in the asymmetric alkylation of aldehydes catalyzed by Zn-aminoalcohols. The molecular descriptors are obtained straightforwardly from the electronic charge density function, rho(r), and the molecular electrostatic potential (MEP) distributions. The chemically meaningful Molecular Shape Field (MSF) descriptor that accounts for the shape properties of the catalyst is defined from rho(r). Alignment independence was achieved by computing the product of the MSF and MEP values of pairs of points over a given distance range on a molecular isosurface and then selecting the product with the highest value. The new QSSR method demonstrated good predictive ability (q2 = 0.79) when full cross-validation procedures were carried out. Accurate predictions were made for a larger data set, although some deviations occurred in the predictions for catalytic systems with low enantiodiscrimination. Analysis of this QSSR model allows for the following: (1) evaluation of the contribution of each functional group to enantioselectivity and (2) the molecular descriptors to be related to previously proposed stereochemical models for the reaction under study.

Mitogen-activated protein kinase-interacting kinases 1 and 2 (MNK1 and MNK2) phosphorylate the oncogene eIF4E on serine 209. This phosphorylation has been reported to be required for its oncogenic activity. To investigate if pharmacological inhibition of MNK1 could be useful for the treatment of cancers, we pursued a comprehensive virtual screening approach to rapidly identify pharmacological tools for target validation and to find optimal starting points for a plausible medicinal chemistry project. A collection of 1236 compounds, selected from a library of 42 168 compounds and a database of 18.8 million structures, were assayed. Of the identified hits, 26 were found to have IC(50) values less than 10 μM (2.10% hit rate). The most potent compound had an IC(50) value of 117 nM, and 73.1% of these hits were fragments. The hits were characterized by a high ligand efficiency (0.32-0.52 kcal/mol per heavy atom). Ten different chemical scaffolds were represented, giving a chemotype/hit ratio of 0.38.

We present a new methodology to predict the enantioselectivity of asymmetric catalysis based on quantitative quadrant-diagram representations of the catalysts and quantitative structure-selectivity relationship (QSSR) modelling. To account for quadrant occupation, we used two types of molecular steric descriptors: the Taft-Charton steric parameter (ν(Charton)) and the distance-weighted volume (V(W) ). By assigning the value of the steric descriptors to each of the positions of the quadrant diagram, we generated the independent variables to build the multidimensional QSSR models. The methodology was applied to predict the enantioselectivity in the cyclopropanation of styrene catalysed by copper complexes. The dataset comprised 30 chiral ligands belonging to four different oxazoline-based ligand families: bis- (Box), azabis- (AzaBox), quinolinyl- (Quinox) and pyridyl-oxazoline (Pyox). In the first-order approximation, we generated QSSR models with good predictive ability (r(2) =0.89 and q(2) =0.88). The derived stereochemical model indicated that placing very large groups at two diagonal quadrants and leaving free the other two might be enough to obtain an enantioselective catalyst. Fitting the data to a higher-order polynomial, which included crossterms between the descriptors of the quadrants, resulted in an improvement of the predicting ability of the QSSR model (r(2) =0.96 and q(2) =0.93). This suggests that the relationship between the steric hindrance and the enantioselectivity is non-linear, and that bulky substituents in diagonal quadrants operate synergistically. We believe that the quantitative quadrant-diagram-based QSSR modelling is a further conceptual tool that can be used to predict the selectivity of chiral catalysts and other aspects of catalytic performance.

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Nucleolar disruption has recently emerged as a relevant means to activate p53 through inhibition of HDM2 by ribosome-free RPL11. Most drugs that induce nucleolar disruption also possess important genotoxic activity, which can have lasting mutagenic effects. Therefore, it is of interest to identify compounds that selectively produce nucleolar disruption in the absence of DNA damage. Here, we have performed a high-throughput screening to search for nucleolar disruptors. We have identified an acridine derivative (PubChem CID-765471) previously known for its capacity to activate p53 independently of DNA damage, although the molecular mechanism underlying p53 activation had remained uncharacterized. We report that CID-765471 produces nucleolar disruption by inhibiting ribosomal DNA transcription in a process that includes the selective degradation of the RPA194 subunit of RNA polymerase I. Following nucleolar disruption, CID-765471 activates p53 through the RPL11/HDM2 pathway in the absence of detectable DNA damage. In a secondary screening of compounds approved for medical use, we identify two additional acridine derivatives, aminacrine and ethacridine, that operate in a similar manner as CID-765471. These findings provide the basis for non-genotoxic chemotherapeutic approaches that selectively target the nucleolus.

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The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.