2 research outputs found
Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis
Chemokine receptors
(CRs) have long been druggable targets for
the treatment of inflammatory diseases and HIV-1 infection. As a powerful
technique, virtual screening (VS) has been widely applied to identifying
small molecule leads for modern drug targets including CRs. For rational
selection of a wide variety of VS approaches, ligand enrichment assessment
based on a benchmarking data set has become an indispensable practice.
However, the lack of versatile benchmarking sets for the whole CRs
family that are able to unbiasedly evaluate every single approach
including both structure- and ligand-based VS somewhat hinders modern
drug discovery efforts. To address this issue, we constructed Maximal
Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs)
using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs
encompasses 13 subtypes out of 20 chemokine receptors, composed of
404 ligands and 15756 decoys so far and is readily expandable in the
future. It had been thoroughly validated that MUBD-hCRs ligands are
chemically diverse while its decoys are maximal unbiased in terms
of “artificial enrichment”, “analogue bias”.
In addition, we studied the performance of MUBD-hCRs, in particular
CXCR4 and CCR5 data sets, in ligand enrichment assessments of both
structure- and ligand-based VS approaches in comparison with other
benchmarking data sets available in the public domain and demonstrated
that MUBD-hCRs is very capable of designating the optimal VS approach.
MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers
major CRs subtypes so far
Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families
Histone
deacetylases (HDACs) are an important class of drug targets
for the treatment of cancers, neurodegenerative diseases, and other
types of diseases. Virtual screening (VS) has become fairly effective
approaches for drug discovery of novel and highly selective histone
deacetylase inhibitors (HDACIs). To facilitate the process, we constructed
maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using
our recently published methods that were originally developed for
building unbiased benchmarking sets for ligand-based virtual screening
(LBVS). The MUBD-HDACs cover all four classes including Class III
(Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors
and 24 609 unbiased decoys. Its ligand sets have been validated
extensively as chemically diverse, while the decoy sets were shown
to be property-matching with ligands and maximal unbiased in terms
of “artificial enrichment” and “analogue bias”.
We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets
against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs
are unique in that they can be applied unbiasedly to both LBVS and
SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore,
to detect the “2D bias” and “LBVS favorable”
effect within the benchmarking sets. In summary, MUBD-HDACs are the
only comprehensive and maximal-unbiased benchmark data sets for HDACs
(including Sirtuins) that are available so far. MUBD-HDACs are freely
available at http://www.xswlab.org/