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

Abstract

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 (IC, EC, K, 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.

A Review on Applications of Computational Methods in Drug Screening and Design

“…In addition, Ferreira da Costa et al [116] report the first PTML (PT + ML) study of a large number of ChEMBL datasets for preclinical determinations of compounds for dopamine pathway proteins. Molecular docking or ML models can be used to solve a specific protein, but these models cannot explain the large and complex large data sets of preclinical assays reported in public databases.…”

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

“…This meta-structure is modeled in order to elucidate the relationships with the disease agents utilizing perturbation models. 45 These models have been proven to be very versatile when applied to infectious diseases, 46 immunological disorders, 47 neurological pathologies, 48 and cancer. 49 Furthermore, the new approach in drug discovery is known as de novo multiscale approach in which a drug is designed within the chemical subspace where it could be deemed beneficial.…”

Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors

“…To solve the aforementioned limitations, several researchers have emphasized the use of interpretable in silico models focused on a combination of perturbation theory concepts and machine learning techniques (PTML) [ 15 , 16 , 17 ], which can integrate different sources of chemical and biological data, enabling the simultaneous prediction of multiple biological endpoints against many targets of varying degrees of complexity. Seminal works on PTML models have found successful applications in diverse research areas such as infectious diseases [ 18 , 19 ], oncology [ 20 , 21 ], neuroscience [ 22 , 23 , 24 , 25 ], proteomics [ 26 ], metabolomics [ 27 ], nanotechnology [ 28 , 29 , 30 , 31 ], toxicology [ 32 ], and immunology and immunotoxicity [ 33 , 34 ].…”