Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l‑Prolyl‑l‑leucyl-glycinamide Peptidomimetics

Predicting
drug–protein interactions (DPIs) for target proteins
involved in dopamine pathways is a very important goal in medicinal
chemistry. We can tackle this problem using Molecular Docking or Machine
Learning (ML) models for one specific protein. Unfortunately, these
models fail to account for large and complex big data sets of preclinical
assays reported in public databases. This includes multiple conditions
of assays, such as different experimental parameters, biological assays,
target proteins, cell lines, organism of the target, or organism of
assay. On the other hand, perturbation theory (PT) models allow us
to predict the properties of a query compound or molecular system
in experimental assays with multiple boundary conditions based on
a previously known case of reference. In this work, we report the
first PTML (PT + ML) study of a large ChEMBL data set of preclinical
assays of compounds targeting dopamine pathway proteins. The best
PTML model found predicts 50000 cases with accuracy of 70–91%
in training and external validation series. We also compared the linear
PTML model with alternative PTML models trained with multiple nonlinear
methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear
model but at the cost of a notable increment of the complexity of
the model. We illustrated the practical use of the new model with
a proof-of-concept theoretical–experimental study. We reported
for the first time the organic synthesis, chemical characterization,
and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition,
we performed a molecular docking study for some of these compounds
with the software Vina AutoDock. The work ends with a PTML model predictive
study of the outcomes of the new compounds in a large number of assays.
Therefore, this study offers a new computational methodology for predicting
the outcome for any compound in new assays. This PTML method focuses
on the prediction with a simple linear model of multiple pharmacological
parameters (IC50, EC50, Ki, etc.) for compounds in assays involving
different cell lines used, organisms of the protein target, or organism
of assay for proteins in the dopamine pathway.