Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

“…Hence, since these scores were also greater than for most of the extensive NPDF hits sharing the same original NP and, in many cases, their original NP (also selected as a hit), it is evident that non-extensive NPDFs have a selective advantage for the models employed in the VS. Interestingly, even if it is intuitive to expect that non-extensive NPDFs could match more features of each model than extensive NPDFs, this hypothesis does not explain why they could exhibit better fit scores than their structurally related NPs. In line with this observation, our results suggest that an appropriate spatial conformation of the features is also critical to avoid problematic steric clashes in the model, including X-vol volumes describing the limits of the target receptor surface, as described in (Toba et al, 2006;Al-Nadaf and Taha, 2011). Consequently, a trade-off between the minimum features to be selected and the configuration required to match them in a 3D space appears to play a key role behind the advantages of the non-extensive fragmentation of NPs.…”

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

“…However, all attempts to achieve statistically successful QSAR models failed, prompting the use of ligand efficiency [LE = −log(IC 50 )/heavy atom count] as an alternative response variable instead of −log(IC 50 ) . The best QSAR models are summarized in Equations and .…”

Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors

“…All our attempts to achieve self‐consistent and predictive MLR‐QSAR models (via GFA‐based descriptor selection) were futile prompting us to evaluate an alternative modeling strategy, namely, to use ligand efficiency as the response variable instead of the conventionally used −log(IC 50 ). Ligand efficiency was successfully used as response variable in QSAR modeling of β‐secreatase inhibitors and renin inhibitors …”

“…Ligand efficiency was successfully used as response variable in QSAR modeling of β-secreatase inhibitors 41 and renin inhibitors. 42…”

“…where log(IC 50 )/HA is ligand efficiency (HA is the number of ligands' heavy atoms), r 2 77 is the correlation coefficient against 77 training compounds, r 2 LOO is the leave-one-out cross-validation correlation coefficient, and r 2 PRESS is the predictive r 2 determined for 19 randomly selected test compounds (1,5,11,14,17,23,32,42,45,50,51,64,67,74,78,128,138,139, and 141, as inTable S1 under supporting information).r 2 m and Δr 2 m are the average and delta r 2 m values calculated for training compounds, respectively. These are recently developed metrics that extensively test the internal and external predictive capacities of a QSAR model through establishing the proximity between predicted and observed response data among training compounds.…”