Labfolder is an electronic lab notebook that enables researchers to record findings and make new discoveries. By reinventing the traditional paper lab notebook, our productivity & collaboration platform makes it easier to create, find, share, discuss & validate research data as a team.
Do you ever get that feeling that you would like to have a magic spell to organize all your data?And once it is organized, wouldn’t it be magnificent if there would be a software that could put together all relevant data from your projects, add some new references and present you with a manuscript draft you can build upon?Good news.(ELN) announced the launch of.Manuscript Writer uses artificial intelligence to prepare a draft of your scientific manuscript based on your data, saving you time and energy to prepare manuscripts to publish. And for now, it is possible to get free access to the add-on.Basically, this just might be the add-on we’ve been dreaming of!Klemen Zupancic, PhD, CEO of sciNote LLC said: “ While the competition within the scientific community to publish articles in high-ranking journals is constantly on the rise, it is also vital that valuable research data are published, and therefore accessible, at the earliest possible time. SciNote’s ELN is already used by over 20,000 scientists to store and manage scientific data. The announcement of this new AI add-on has the potential to transform the article writing process and empower the scientists.”To use the Manuscript Writer, you first need to create your free account in sciNote and organize your data by projects – no worries, sciNote makes the whole thing really easy and flexible. There’s a to fill out and you’re in. Free account, forever. No hidden small text.In sciNote, data is structured by: Projects — Experiments — Tasks — Protocols.
Your work starts with creating your projects on the dashboard.
Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule‐based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10‐accuracy of 95% in retrosynthesis and 97% for reaction prediction on a validation set of almost 1 million reactions. As a service to our authors and readers, this journal provides supporting information supplied by the authors.
Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. FilenameDescription455.7 KBSupplementaryPlease note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.