4 research outputs found
Application of Quantitative Structure–Activity Relationship Models of 5‑HT<sub>1A</sub> Receptor Binding to Virtual Screening Identifies Novel and Potent 5‑HT<sub>1A</sub> Ligands
The
5-hydroxytryptamine 1A (5-HT<sub>1A</sub>) serotonin receptor
has been an attractive target for treating mood and anxiety disorders
such as schizophrenia. We have developed binary classification quantitative
structure–activity relationship (QSAR) models of 5-HT<sub>1A</sub> receptor binding activity using data retrieved from the PDSP <i>K</i><sub>i</sub> database. The prediction accuracy of these
models was estimated by external 5-fold cross-validation as well as
using an additional validation set comprising 66 structurally distinct
compounds from the World of Molecular Bioactivity database. These
validated models were then used to mine three major types of chemical
screening libraries, i.e., drug-like libraries, GPCR targeted libraries,
and diversity libraries, to identify novel computational hits. The
five best hits from each class of libraries were chosen for further
experimental testing in radioligand binding assays, and nine of the
15 hits were confirmed to be active experimentally with binding affinity
better than 10 ÎĽM. The most active compound, Lysergol, from
the diversity library showed very high binding affinity (<i>K</i><sub>i</sub>) of 2.3 nM against 5-HT<sub>1A</sub> receptor. The novel
5-HT<sub>1A</sub> actives identified with the QSAR-based virtual screening
approach could be potentially developed as novel anxiolytics or potential
antischizophrenic drugs
Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification
Traditional read-across approaches
typically rely on the chemical
similarity principle to predict chemical toxicity; however, the accuracy
of such predictions is often inadequate due to the underlying complex
mechanisms of toxicity. Here, we report on the development of a hazard
classification and visualization method that draws upon both chemical
structural similarity and comparisons of biological responses to chemicals
measured in multiple short-term assays (“biological”
similarity). The Chemical–Biological Read-Across (CBRA) approach
infers each compound’s toxicity from both chemical and biological
analogues whose similarities are determined by the Tanimoto coefficient.
Classification accuracy of CBRA was compared to that of classical
RA and other methods using chemical descriptors alone or in combination
with biological data. Different types of adverse effects (hepatotoxicity,
hepatocarcinogenicity, mutagenicity, and acute lethality) were classified
using several biological data types (gene expression profiling and
cytotoxicity screening). CBRA-based hazard classification exhibited
consistently high external classification accuracy and applicability
to diverse chemicals. Transparency of the CBRA approach is aided by
the use of radial plots that show the relative contribution of analogous
chemical and biological neighbors. Identification of both chemical
and biological features that give rise to the high accuracy of CBRA-based
toxicity prediction facilitates mechanistic interpretation of the
models
Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
Prior to using a quantitative structure activity relationship
(QSAR)
model for external predictions, its predictive power should be established
and validated. In the absence of a true external data set, the best
way to validate the predictive ability of a model is to perform its
statistical external validation. In statistical external validation,
the overall data set is divided into training and test sets. Commonly,
this splitting is performed using random division. Rational splitting
methods can divide data sets into training and test sets in an intelligent
fashion. The purpose of this study was to determine whether rational
division methods lead to more predictive models compared to random
division. A special data splitting procedure was used to facilitate
the comparison between random and rational division methods. For each
toxicity end point, the overall data set was divided into a modeling
set (80% of the overall set) and an external evaluation set (20% of
the overall set) using random division. The modeling set was then
subdivided into a training set (80% of the modeling set) and a test
set (20% of the modeling set) using rational division methods and
by using random division. The Kennard-Stone, minimal test set dissimilarity,
and sphere exclusion algorithms were used as the rational division
methods. The hierarchical clustering, random forest, and <i>k</i>-nearest neighbor (<i>k</i>NN) methods were used to develop
QSAR models based on the training sets. For <i>k</i>NN QSAR,
multiple training and test sets were generated, and multiple QSAR
models were built. The results of this study indicate that models
based on rational division methods generate better statistical results
for the test sets than models based on random division, but the predictive
power of both types of models are comparable
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>