As a critical issue in drug development
and postmarketing
safety surveillance, drug-induced liver injury (DILI) leads to failures
in clinical trials as well as retractions of on-market approved drugs.
Therefore, it is important to identify DILI compounds in the early-stages
through in silico and in vivo studies. It is difficult using conventional
safety testing methods, since the predictive power of most of the
existing frameworks is insufficiently effective to address this pharmacological
issue. In our study, we employ a natural language processing (NLP)
inspired computational framework using convolutional neural networks
and molecular fingerprint-embedded features. Our development set and
independent test set have 1597 and 322 compounds, respectively. These
samples were collected from previous studies and matched with established
chemical databases for structural validity. Our study comes up with
an average accuracy of 0.89, Matthews’s correlation coefficient
(MCC) of 0.80, and an AUC of 0.96. Our results show a significant
improvement in the AUC values compared to the recent best model with
a boost of 6.67%, from 0.90 to 0.96. Also, based on our findings,
molecular fingerprint-embedded featurizer is an effective molecular
representation for future biological and biochemical studies besides
the application of classic molecular fingerprints.
The human cytochrome P450 (CYP) superfamily
holds responsibilities
for the metabolism of both endogenous and exogenous compounds such
as drugs, cellular metabolites, and toxins. The inhibition exerted
on the CYP enzymes is closely associated with adverse drug reactions
encompassing metabolic failures and induced side effects. In modern
drug discovery, identification of potential CYP inhibitors is, therefore,
highly essential. Alongside experimental approaches, numerous computational
models have been proposed to address this biochemical issue. In this
study, we introduce iCYP-MFE, a computational framework for virtual
screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms.
iCYP-MFE contains a set of five robust, stable, and effective prediction
models developed using multitask learning incorporated with molecular
fingerprint-embedded features. The results show that multitask learning
can remarkably leverage useful information from related tasks to promote
global performance. Comparative analysis indicates that iCYP-MFE achieves
three predominant tasks, one equivalent task, and one less effective
task compared to state-of-the-art methods. The area under the receiver
operating characteristic curve (AUC-ROC) and the area under the precision-recall
curve (AUC-PR) were two decisive metrics used for model evaluation.
The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC
value of 0.93 while the prediction task for CYP1A2-inhibition obtains
the highest AUC-PR value of 0.92. The substructural analysis preliminarily
explains the nature of the CYP-inhibitory activity of compounds. An
online web server for iCYP-MFE with a user-friendly interface was
also deployed to support scientific communities in identifying CYP
inhibitors.
Traditional herbal medicine has been
an inseparable part of the
traditional medical science in many countries throughout history.
Nowadays, the popularity of using herbal medicines in daily life,
as well as clinical practices, has gradually expanded to numerous
Western countries with positive impacts and acceptance. The continuous
growth of the herbal consumption market has promoted standardization
and modernization of herbal-derived products with present pharmacological
criteria. To store and extensively share this knowledge with the community
and serve scientific research, various herbal metabolite databases
have been developed with diverse focuses under the support of modern
advances. The advent of these databases has contributed to accelerating
research on pharmaceuticals of natural origins. In the scope of this
study, we critically review 30 herbal metabolite databases, discuss
different related perspectives, and provide a comparative analysis
of 18 accessible noncommercial ones. We hope to provide you with fundamental
information and multidimensional perspectives from herbal medicines
to modern drug discovery.
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