首页|Data from University of Pittsburgh Broaden Understanding of Machine Learning (Te aching old docks new tricks with machine learning enhanced ensemble docking)
Data from University of Pittsburgh Broaden Understanding of Machine Learning (Te aching old docks new tricks with machine learning enhanced ensemble docking)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om the University of Pittsburgh by NewsRx correspondents, research stated, “We h ere introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS).” Funders for this research include National Institute of General Medical Sciences . Our news journalists obtained a quote from the research from University of Pitts burgh: “Ensemble VS is an established method for predicting protein/small-molecu le (ligand) binding. Unlike traditional VS, which focuses on a single protein co nformation, ensemble VS better accounts for protein flexibility by predicting bi nding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single sc ore. To effectively rank and prioritize the molecules for further evaluation (in cluding experimental testing), researchers must select which protein conformatio ns to consider and how best to map each compound’s spectrum of scores to a singl e value, decisions that are system-specific. EnOpt uses machine learning to addr ess these challenges. We perform benchmark VS to show that for many systems, EnO pt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods.”
University of PittsburghCyborgsEmerg ing TechnologiesMachine Learning
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