首页|Data on Machine Learning Reported by Gengmo Zhou and Colleagues (Bridging Machin e Learning and Thermodynamics for Accurate pK a Prediction)
Data on Machine Learning Reported by Gengmo Zhou and Colleagues (Bridging Machin e Learning and Thermodynamics for Accurate pK a Prediction)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting originating in Beijing, People's Republi c of China, by NewsRx journalists, research stated, "Integrating scientific prin ciples into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. He rein, we introduce Uni-p , a novel framework that successfully incorporates ther modynamic principles into machine learning modeling, achieving highprecision pr edictions of acid dissociation constants (p ), a crucial task in the rational de sign of drugs and catalysts, as well as a modeling challenge in computational ph ysical chemistry for small organic molecules." The news reporters obtained a quote from the research, "Uni-p utilizes a compreh ensive free energy model to represent molecular protonation equilibria accuratel y. It features a structure enumerator that reconstructs molecular configurations from p data, coupled with a neural network that functions as a free energy pred ictor, ensuring high-throughput, data-driven prediction while preserving thermod ynamic consistency."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningPhysicsThermodynamics
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