Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set. File list (3) download file view on ChemRxiv manuscript.pdf (4.23 MiB) download file view on ChemRxiv supplementary.pdf (0.92 MiB) download file view on ChemRxiv tables.zip (5.99 KiB)

Purpose: The mesenchymal-epithelial transition factor (c-Met) receptor, also known as hepatocyte growth factor receptor (HGFR), controls morphogenesis, a process that is physiologically required for embryonic development and tissue repair. Aberrant c-Met activation is associated with a variety of human malignancies including cancers of the lung, kidney, stomach, liver, and brain. In this study, we investigated the properties of two novel compounds developed to selectively inhibit the c-Met receptor in antitumor therapeutic interventions.Experimental Design: The pharmacologic properties, c-Met inhibitory activity, and antitumor effects of EMD 1214063 and EMD 1204831 were investigated in vitro and in vivo, using human cancer cell lines and mouse xenograft models.Results: EMD 1214063 and EMD 1204831 selectively suppressed the c-Met receptor tyrosine kinase activity. Their inhibitory activity was potent [inhibitory 50% concentration (IC 50 ), 3 nmol/L and 9 nmol/L, respectively] and highly selective, when compared with their effect on a panel of 242 human kinases. Both EMD 1214063 and EMD 1204831 inhibited c-Met phosphorylation and downstream signaling in a dose-dependent fashion, but differed in the duration of their inhibitory activity. In murine xenograft models, both compounds induced regression of human tumors, regardless of whether c-Met activation was HGF dependent or independent. Both drugs were well tolerated and induced no substantial weight loss after more than 3 weeks of treatment.Conclusions: Our results indicate selective c-Met inhibition by EMD 1214063 and EMD 1204831 and strongly support clinical testing of these compounds in the context of molecularly targeted anticancer strategies.

No abstract

Eubacterial tRNA-guanine transglycosylase (TGT) is involved in the hypermodification of cognate tRNAs, leading to the exchange of G34 by preQ1 at the wobble position in the anticodon loop. Mutation of the tgt gene in Shigella flexneri results in a significant loss of pathogenicity of the bacterium due to inefficient translation of a virulence protein mRNA. Herein, we describe the discovery of a ligand with an unexpected binding mode. On the basis of this binding mode, three slightly deviating pharmacophore hypotheses have been derived. Virtual screening based on this composite pharmacophore model retrieved a set of potential TGT inhibitors belonging to several compound classes. All nine tested inhibitors being representatives of these classes showed activity in the micromolar range, two of them even in the submicromolar range.

No abstract

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

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