Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia

TL;DR

This study employs proteomic learning and machine learning techniques to identify novel drug candidates targeting GABA receptors for improved and safer anesthesia, integrating extensive protein interaction data and compound screening.

Contribution

It introduces a novel proteomic learning framework combining PPI networks and NLP-enhanced machine learning to discover potential anesthetic agents targeting GABA receptors.

Findings

Identified potential lead compounds with favorable ADMET profiles.

Screened over 180,000 drug candidates targeting GABRA5.

Provided insights into structure optimization of existing anesthetics.

Abstract

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a dataset comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformer and autoencoder embeddings. Through a comprehensive screening process, we evaluated the side effects and repurposing potential of over 180,000 drug candidates targeting the GABRA5 receptor. Additionally, we assessed the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify those with near-optimal characteristics. This approach also involved optimizing the structures of existing anesthetics. Our work presents an innovative strategy for the development of new anesthetic drugs, optimization of anesthetic use, and deeper understanding of potential anesthesia-related side effects.