Abstract Both human and rodent data showing reduced expression of the small-conductance, calcium- activated potassium channel KCa2.2 (SK2) in the context of morphine and alcohol dependence and withdrawal suggest KCa2 channel activation as a promising therapeutic approach for the treatment of substance use disorders and associated comorbidities. In support of this therapeutic hypothesis, KCa2 channel activators reduce alcohol seeking and intake in rats, while direct injection of the KCa2 channel blocking peptide apamin into the nucleus accumbens, a brain region which plays a crucial role in regulating craving and drug seeking during abstinence, increases alcohol intake in mice. In response to the HEAL Initiative RFA-DA-22-032 Funding Opportunity Announcement, we are here proposing to perform virtual high-throughput-screening with the goal of identifying novel KCa2 activator pharmacophores that are free of the liabilities of the existing unselective benzothiazole-type activators and that could be developed into innovative treatments for opioid use disorders (OUD), a condition that affects more than 3 million Americans. For the implementation of this project, we will draw on our 20 years of experience with the medicinal chemistry of KCa channels. After we initially developed KCa3.1 blockers such as TRAM-34, we later discovered the mixed KCa2/3 activator SKA-31, and the KCa3.1 selective activators SKA-121 and SKA-111. All these compounds, which have been widely used in the field to probe the physiological and pathophysiological roles of KCa channels were designed using classical medicinal chemistry approaches without any structural insight. However, the MacKinnon laboratory recently published the full-length cryo-EM structure of a KCa channel in the closed and two open states and we now have a high-quality structural template available to virtually screen for novel KCa2.2 channel modulators that could be used as pharmacological probes and as leads for the design of drugs for the treatment of OUD. In Aim-1, we will validate our KCa3.1-based KCa2.2 homology model by virtually screening 2000 FDA-approved compounds in two activator binding pockets and experimentally confirm the hits by electrophysiology and mutagenesis. In Aim-2, we will perform a virtual High Throughput Screen (vHTS) of a larger, 130,000-compound library with machine learning (ML) techniques for pose classification and binding affinity prediction. Taken together, these two aims have the potential of 1) identifying a clinically used drug that could be repurposed for OUD and 2) discovering novel KCa2.2 activator pharmacophores that could serve as templates for structure- based-medicinal chemistry optimization aimed at developing OUD treatments with a novel mechanism of action.