Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets

Successful
drug discovery projects require control and optimization
of compound properties related to pharmacokinetics, pharmacodynamics,
and safety. While volume and chemotype coverage of public and corporate
ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity)
databases are constantly growing, deep neural nets (DNN) emerged as
transformative artificial intelligence technology to analyze those
challenging data. Relevant features are automatically identified,
while appropriate data can also be combined to multitask networks
to evaluate hidden trends among multiple ADME-Tox parameters for implicitly
correlated data sets. Here we describe a novel, fully industrialized
approach to parametrize and optimize the setup, training, application,
and visual interpretation of DNNs to model ADME-Tox data. Investigated
properties include microsomal lability in different species, passive
permeability in Caco-2/TC7 cells, and logD. Statistical models are
developed using up to 50 000 compounds from public or corporate
databases. Both the choice of DNN hyperparameters and the type and
quantity of molecular descriptors were found to be important for successful
DNN modeling. Alternate learning of multiple ADME-Tox properties,
resulting in a multitask approach, performs statistically superior
on most studied data sets in comparison to DNN single-task models
and also provides a scalable method to predict ADME-Tox properties
from heterogeneous data. For example, predictive quality using external
validation sets was improved from R2 of
0.6 to 0.7 comparing single-task and multitask DNN networks from human
metabolic lability data. Besides statistical evaluation, a new visualization
approach is introduced to interpret DNN models termed “response
map”, which is useful to detect local property gradients based
on structure fragmentation and derivatization. This method is successfully
applied to visualize fragmental contributions to guide further design
in drug discovery programs, as illustrated by CRCX3 antagonists and
renin inhibitors, respectively.