This paper presents the teaching strategy known as problem-based learning as an innovation implemented in the practical experiences of the Organic Chemistry course (Bachelor of Genetics), Faculty of Exact, Chemical and Natural Sciences (Universidad Nacional de Misiones, Argentina). It reviews the ...
The shift to distance teaching and learning during the COVID-19 pandemic brought about a real challenge for both instructors and students. To face these difficulties in teaching undergraduate Chemistry courses at the University of Santo Tomas, a blended learning strategy in the context of teaching ...
Learning in continuous action space for developing high dimensional potential energy models Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the...
augmentation strategy discuss the effectiveness of these modules. Additionally, we provide a case study illustrating how MSMS-GT focuses on multi-scale molecular structures, which can be seen in Supplementary Information Note2. Table 2 Performance of our RetroExplainer and the state-of-the-art method...
A community-powered search of machine learning strategy space to find NMR property prediction models,Lars A. Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Guillaume Huard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbl...
December 5, 2022Organic Chemistry Using a Previewing Strategy to Help Students Get the Most Out of Reading When I was an undergraduate student, I hated reading my chemistry textbooks. Like many science faculty, my professors would assign sections of the textbook to read before class with little ...
When using DL to predict the protein structure from a gene sequence, VAE can be used to encode compound structures and then be combined with the GAN for learning (Lin, 2009; Widera, 2010; Yu et al., 2015). With switch networks formed by atoms, strategy networks and Monte Carlo trees ...
Existing methods for LBS prediction are based on variety of algorithmic approaches. Traditionally, methods have been categorized based on their main algorithmic strategy into geometric, energetic, conservation based, template based (the last two also sometimes referred to as evolutionary) and machine lea...
responses. A more recent study by Merck14, used the same scaling strategy to model an in-house crowdsourced proxy for molecular complexity. While varying low to fair correlation degrees were found between the scores assigned by the chemists in the previous two studies, the reported study designs...
There is an interesting phenomenon that RFE is the most common feature selection method in the ML workflow for predicting formability and stability. In some application scenarios, GA is also a more effective feature selection method. Xu et al. [13] proposed a multi-properties ML strategy to ...