首页|Investigators at University of Munster Discuss Findings in Machine Learning (An Evolutionary Algorithm for Interpretable Molecular Representations)
Investigators at University of Munster Discuss Findings in Machine Learning (An Evolutionary Algorithm for Interpretable Molecular Representations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Machine Learning. According to news reporting from Munster, Germany, by NewsRx journalists, resear ch stated, “Encoding molecular structures into a computer - readable, utilizable format is the key step for any machine learning application in all chemical scie nces. Current representations vary strongly in complexity and shape, depending o n the application.” Financial support for this research came from German Research Foundation (DFG). The news correspondents obtained a quote from the research from the University o f Munster, “Therefore, the number of domain -specific representations is rapidly growing, with some being altered and retuned constantly. These tailored represe ntations raise the barriers for entry and method adaption, thus decelerating pro gress in application. Herein, we present a general algorithm capable of yielding a highly specific representation solely based on a given dataset. The algorithm utilizes structural queries and evolutionary methodologies to generate interpre table molecular fingerprints. These are highly suited for molecular machine lear ning, enabling the accurate prediction of reactivity, property, and biological a ctivity. We demonstrate its native interpretability, allowing for the extraction of knowledge, such as reactivity trends.”
MunsterGermanyEuropeAlgorithmsCy borgsEmerging TechnologiesEvolutionary AlgorithmMachine LearningMathemat icsUniversity of Munster
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