2 research outputs found
Quantum Mechanics-Based Properties for 3D-QSAR
We have used a set of four local
properties based on semiempirical
molecular orbital calculations (electron density (ρ), hydrogen
bond donor field (HDF), hydrogen bond acceptor field (HAF), and molecular
lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the
limitations of the current force field-based molecular interaction
fields (MIFs). These properties can be calculated rapidly and are
thus amenable to high-throughput industrial applications. Their statistical
performance was compared with that of conventional 3D-QSAR approaches
using nine data sets (angiotensin converting enzyme inhibitors (ACE),
acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands
(BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase
inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin
inhibitors (THER), thrombin inhibitors (THR), and serine protease
factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested
thoroughly for robustness and predictive ability. The average performance
of the quantum mechanical molecular interaction field (QM-MIF) models
for the nine data sets is better than that of the conventional force
field-based MIFs. In the individual data sets, the QM-MIF models always
perform better than, or as well as, the conventional approaches. It
is particularly encouraging that the relative performance of the QM-MIF
models improves in the external validation. In addition, the models
generated showed statistical stability with respect to model building
procedure variations such as grid spacing size and grid orientation.
QM-MIF contour maps reproduce the features important for ligand binding
for the example data set (factor Xa inhibitors), demonstrating the
intuitive chemical interpretability of QM-MIFs