3 research outputs found
Chembench: A Publicly Accessible, Integrated Cheminformatics Portal
The
enormous increase in the amount of publicly available chemical
genomics data and the growing emphasis on data sharing and open science
mandates that cheminformaticians also make their models publicly available
for broad use by the scientific community. Chembench is one of the
first publicly accessible, integrated cheminformatics Web portals.
It has been extensively used by researchers from different fields
for curation, visualization, analysis, and modeling of chemogenomics
data. Since its launch in 2008, Chembench has been accessed more than
1 million times by more than 5000 users from a total of 98 countries.
We report on the recent updates and improvements that increase the
simplicity of use, computational efficiency, accuracy, and accessibility
of a broad range of tools and services for computer-assisted drug
design and computational toxicology available on Chembench. Chembench
remains freely accessible at https://chembench.mml.unc.ed
Chemotext: A Publicly Available Web Server for Mining Drug–Target–Disease Relationships in PubMed
Elucidation of the
mechanistic relationships between drugs, their
targets, and diseases is at the core of modern drug discovery research.
Thousands of studies relevant to the drug–target–disease
(DTD) triangle have been published and annotated in the Medline/PubMed
database. Mining this database affords rapid identification of all
published studies that confirm connections between vertices of this
triangle or enable new inferences of such connections. To this end,
we describe the development of Chemotext, a publicly available Web
server that mines the entire compendium of published literature in
PubMed annotated by Medline Subject Heading (MeSH) terms. The goal
of Chemotext is to identify all known DTD relationships and infer
missing links between vertices of the DTD triangle. As a proof-of-concept,
we show that Chemotext could be instrumental in generating new drug
repurposing hypotheses or annotating clinical outcomes pathways for
known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu
Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
Multiple
approaches to quantitative structure–activity relationship
(QSAR) modeling using various statistical or machine learning techniques
and different types of chemical descriptors have been developed over
the years. Oftentimes models are used in consensus to make more accurate
predictions at the expense of model interpretation. We propose a simple,
fast, and reliable method termed Multi-Descriptor Read Across (MuDRA)
for developing both accurate and interpretable models. The method
is conceptually related to the well-known kNN approach but uses different
types of chemical descriptors simultaneously for similarity assessment.
To benchmark the new method, we have built MuDRA models for six different
end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG
liability, skin sensitization, and endocrine disruption) and compared
the results with those generated with conventional consensus QSAR
modeling. We find that models built with MuDRA show consistently high
external accuracy similar to that of conventional QSAR models. However,
MuDRA models excel in terms of transparency, interpretability, and
computational efficiency. We posit that due to its methodological
simplicity and reliable predictive accuracy, MuDRA provides a powerful
alternative to a much more complex consensus QSAR modeling. MuDRA
is implemented and freely available at the Chembench web portal (https://chembench.mml.unc.edu/mudra<i>)</i>