首页|Shanghai Jiao Tong University Reports Findings in Machine Learning (Enhanced Sam pling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling an d Machine Learning)
Shanghai Jiao Tong University Reports Findings in Machine Learning (Enhanced Sam pling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling an d Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Shanghai, Pe ople’s Republic of China, by NewsRx correspondents, research stated, “Biomolecul ar simulations often suffer from the ‘time scale problem’, hindering the study o f rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a signifi cant challenge.” Our news editors obtained a quote from the research from Shanghai Jiao Tong Univ ersity, “Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitati ons. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are i dentified using Koopman-reweighted time-lagged independent component analysis (K TICA), which are subsequently leveraged by on-the-fly probability enhanced sampl ing (OPES) to efficiently explore the FES.”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine Learning
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