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.
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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