Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

Some targets predicted
by machine learning (ML) in drug discovery
remain a challenge because of poor prediction. In this study, a new
prediction model was developed and rat clearance (CL) was selected
as a target because it is difficult to predict. A classification model
was constructed using 1545 in-house compounds with rat CL data. The
molecular descriptors calculated by Molecular Operating Environment
(MOE), alvaDesc, and ADMET Predictor software were used to construct
the prediction model. In conventional ML using 100 descriptors and
random forest selected by DataRobot, the area under the curve (AUC)
and accuracy (ACC) were 0.883 and 0.825, respectively. Conversely,
the prediction model using DeepSnap and Deep Learning (DeepSnap-DL)
with compound features as images had AUC and ACC of 0.905 and 0.832,
respectively. We combined the two models (conventional ML and DeepSnap-DL)
to develop a novel prediction model. Using the ensemble model with
the mean of the predicted probabilities from each model improved the
evaluation metrics (AUC = 0.943 and ACC = 0.874). In addition, a consensus
model using the results of the agreement between classifications had
an increased ACC (0.959). These combination models with a high level
of predictive performance can be applied to rat CL as well as other
pharmacokinetic parameters, pharmacological activity, and toxicity
prediction. Therefore, these models will aid in the design of more
rational compounds for the development of drugs.