Machine-Learning Model Predicts Interaction with γ-Amino Butyric Acid (GABA)ergic Neurotransmission

Abstract

Introduction:

We are developing computational models for basic nervous system pathways for use in toxicology, pharmacology, and medicine. γ-Amino butyric acid (GABA) is the major inhibitory neurotransmitter in the central nervous system, and acts through GABA_A (cys-loop) or GABA_B (G-protein–coupled) receptors.

Materials and Methods:

We mined publicly available data for compounds reported to interact (actives) or not interact (negative controls) with either of these receptors, and used these data to train binary random-forest machine-learning models to predict such interactions for novel compounds. Using the Konstanz Information Miner (KNIME), we developed two types of models for interaction with these receptors; both types derive and analyze structural fingerprints, and conserved scaffolds with the second type also incorporating information on binding energies and topologies from protein–ligand docking.

Results and Discussion:

The scaffold/fingerprint-based models were highly sensitive (85.5%–93.5%) and accurate (89.8%–94.0%). Incorporation of pharmacophore data (derived from docking) showed sensitivities of 89.7%–96.5% and improved the GABA_B model, which had fewer active compounds (330 vs. >4000 for the GABA_A receptor). The models exhibited high positive and negative predictivity (82.6%–97.5%). Although robust sensitivity was achieved for the GABA_A receptor with 17% of available data in a fingerprint-based model, sensitivity for the less well-studied GABA_B receptor was lower. We anticipate that model performance for GABA_B should improve with the generation of additional data.

Conclusion:

The models can be used in an integrated testing approach to complement in vitro screening and as a covariate in Quantitative Structure Activity Relationship ((Q)SAR) models for other neurological endpoints.