In silico ADMET models have progressed significantly over the past ~4 decades, but
still, the pharmaceutical industry is vexed by the late-stage toxicity failure of lead molecules. This
problem of late-stage attrition of the drug candidates because of adverse ADMET profile motivated
us to analyze the current role and status of different in silico tools along with the rise of machine
learning (ML) based program for ADMET prediction. In this review, we have differentiated
AI from traditional in silico tools because, unlike traditional in silico tools where the final decision
is made manually, AI automates the decision-making prerogative of humans. Due to the large volume
of literature in this field, we have considered the publications in the last two years for our review.
Overall, from the literature reviewed, deep neural networks (DNN) algorithm or deep learning
seems to be the future of ML-based prediction models. DNNs have shown the ability to learn
from more complex data and this gives DNN an edge over other ML algorithms to be applied for
ADMET prediction. Our result also suggests that we need closer collaboration between the ADMET
data generators and those who are employing ML-based tools on this generated data to build
predictive models, so that more accurate models could be developed. Overall, our study concludes
that ML is still a work in progress and its appetite for data has not been sated yet. It needs loads of
more quality data and still some time to prove its real worth in predicting ADMET.