Hybrid Machine-Learning/SMARTS Profiling Model for Mitochondrial Inhibition

Abstract

Introduction:

We are using big-data mining to develop computational models that predict whether previously uncharacterized compounds will or will not target important biological pathways. Mitochondria play essential life-sustaining roles, with their dysfunction linked to diverse pathologies.

Materials and Methods:

We built a mitochondrial inhibition model that combines molecular scaffolding and fingerprinting of a large database compiled primarily from in vitro high-throughput screening (HTS) data. We refined the model to include SMARTS profilers for known subtarget features (i.e., inhibition of Complexes I–V or uncoupling through protonophore action). For some of these, compound substructures capable of metabolic transformation to cyanide or hydrogen sulfide were identified and included based on potential for high in vivo toxicity despite lack of activity in cell-based platforms that appear to lack critical metabolic pathways and/or cell sensitivity.

Results and Discussion:

The model is comprehensive—the machine-learning component has high sensitivity (80.3%) and accuracy (79.4%), together with positive and negative predictive values of 60.9% and 90.7%, respectively. Model predictivity is limited by the heterogeneity of mechanistic mitochondrial targets, as well as sensitivity of HTS assays and domain of tested compounds. When applied to a database of human therapeutics withdrawn from the market due to liver injury, it identified all compounds demonstrated to target mitochondria.

Conclusions:

The model can be used in an integrated approach to complement early in vitro screening and as a covariate in Quantitative Structure Activity Relationship (QSAR) models for systemic toxicological end points.