Due: July 26, 2024
The goal is to leverage AI/ML tools to identify pharmacotherapeutic development candidates with lower toxicity and higher efficacy to prevent or treat SUDs. Molecules may include new chemical entities, investigational compounds, and repurposed marketed medications. AI/ML tools can pinpoint the most promising targets, design effective ligands based on predicted drug-likeness, and guide in vitro and in vivo assays to assess the effects of these ligands on biological targets and functions. Applicants should propose and conduct activities that use AI/ML tools to streamline, enhance decision-making, and accelerate the identification of SUD pharmacotherapeutics. Applications may aim to conduct the following process: Identify and validate disease targets. Screen potential compounds to develop preliminary hits. Develop assays to test the activities of candidate compounds in vitro. Synthesize novel series of compounds; test efficacy and toxicities in vitro. Test pharmacokinetics and toxicity of selected compounds in relevant in vivo models on a non-GLP level. Conduct non-GLP in vivo toxicity and efficacy of lead compound; pharmacokinetic studies.