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Remdesivir, an antiviral drug, became the only medication approved by the U.S. Food and Drug Administration (FDA) to treat symptoms of COVID-19 in October 2020. While its effectiveness is debated, authorizing an alternative treatment is a difficult process requiring a series of laboratory tests. Through computerized modeling, it may be possible to discover compounds even more effective against COVID-19 than remdesivir. Discovering these substances computationally can be a precursor to experimental testing.
Finding potential COVID-19 drugs through computational models was the goal of Sayalee Patankar, a then high school student in Illinois, United States. The research project used machine learning and computational docking—a method of predicting the orientation of a molecule when it binds to another. The goal was to find compounds to bind to a specific protein in coronaviruses to inhibit their function. Sayalee’s research project discovered five unique compounds potentially more effective against COVID-19 than remdesivir.
This project wasn’t Sayalee’s first experience in research, having previously participated in science fairs and performing similar research on the Zika virus. After writing her paper, Sayalee shared her preprint through an online platform.
As a young researcher, Sayalee says she didn’t attempt peer review because she “assumed it would be really hard since [she] was a single author and it was a high school paper.” She also worried about the strictly computational nature of her work when approaching journals: “I was hearing other people who had solely computational work, and journals were hesitant to accept them because they didn’t have any experimental backings.”
Nevertheless, Sayalee’s paper - which got her placed among the top 300 scholars in the Regeneron Science Talent Search - has received 14 citations since publication. These citations include several literature reviews focusing on artificial intelligence and machine learning role at the forefront of combatting COVID-19.
Sayalee is optimistic about Jinso, a platform to connect high school students with research opportunities, to help researchers like herself. Jinso supports researchers throughout the entire research process, including allowing them to easily share completed papers.
Another difficulty that Sayalee faced was finding a research mentor, noting, “it’s been very hard to find mentors who are willing to work with you, especially as a high schooler.” Even beyond high school, finding an advisor can be difficult. “Professors are already reluctant to take in undergrads at their own institution,” she added.
A key function of Jinso’s network is connecting researchers with more experienced mentors. For researchers like herself, Sayalee believes that connecting with mentors “[is] the biggest [benefit]” to using Jinso. Having research mentors is the key to boosting the number of undergraduate and pre-university students engaged in research: “finding people who are willing to help you at such a young age is really helpful.”
This fall, Sayalee plans to begin her undergraduate studies at Harvard University, where she will concentrate in Chemical and Physical Biology. Staying involved in drug research is something that she strongly considers.
GitHub is a popular platform used by computer scientists to manage their collaborative projects, but a similar program does not exist for academic work. There is no standard platform to create work, connect with others, and share work in one place. Most platforms only fall into one or two of these categories.The Jinso collaboration tool is a better way for groups to work on projects. By bringing the entire academic collaboration process onto one tool, it simplifies workflows and communication.The first steps for using the Jinso platform are:
Once a user builds a network, they can create new Groups that consist of their network members. By default, the creator of a group is the admin. The most common Group is a research group, but the platform can manage several other types of academic projects. Platform users can create study groups for sharing course materials or groups of club members for extracurricular work.The admin of the Group has the ability to add new members at any time.
Admins are also responsible for creating Projects within Groups.
A Project for a research group is usually a research paper, but Projects can also be other forms of documents that could benefit from discussion and revisions. Examples include study guides, business plans, articles, and essays. Each Group can have an unlimited number of Projects within it, and all Projects within a Group are shared among the same members.
Once a user builds a network, they can create new Groups that consist of their network members. By default, the creator of a group is the admin. The most common Group is a research group, but the platform can manage several other types of academic projects.
Platform users can create study groups for sharing course materials or groups of club members for extracurricular work.The admin of the Group has the ability to add new members at any time. Admins are also responsible for creating Projects within Groups.
A Project for a research group is usually a research paper, but Projects can also be other forms of documents that could benefit from discussion and revisions. Examples include study guides, business plans, articles, and essays. Each Group can have an unlimited number of Projects within it, and all Projects within a Group are shared among the same members.
When a new Project is created, an initial revision must be shared. This can either be plain text or a PDF.
The Project will be immediately visible to all Group members with the first revision shown. Group members can comment on the revision with questions or feedback, and others can reply to comments.When another revision of the paper has been completed, the Group admin can add a new revision to the same Project.
The revision will become visible above the prior revision, and it will have a new comment box associated with it. Projects make it simple to keep track of a paper’s entire revision history and discussions at each stage.
For each revision, Group admins can also create subtasks. Arrows allow Group members to view all of the different subtasks and comment on them individually. Subtasks allow a paper to be analyzed in unique components. For example, a research paper can have a unique subtask for each of its sections, and collaborators can discuss them all separately in the comment boxes. Jinso is a quicker way to collaborate on long-term projects. It makes it easier to connect, share, and manage the development of ideas and papers. You can create a Jinso account and start using the platform today for your research and academic needs at jinso.io.
Remdesivir, an antiviral drug, became the only medication approved by the U.S. Food and Drug Administration (FDA) to treat symptoms of COVID-19 in October 2020. While its effectiveness is debated, authorizing an alternative treatment is a difficult process requiring a series of laboratory tests. Through computerized modeling, it may be possible to discover compounds even more effective against COVID-19 than remdesivir. Discovering these substances computationally can be a precursor to experimental testing.
Finding potential COVID-19 drugs through computational models was the goal of Sayalee Patankar, a then high school student in Illinois, United States. The research project used machine learning and computational docking—a method of predicting the orientation of a molecule when it binds to another. The goal was to find compounds to bind to a specific protein in coronaviruses to inhibit their function. Sayalee’s research project discovered five unique compounds potentially more effective against COVID-19 than remdesivir.
This project wasn’t Sayalee’s first experience in research, having previously participated in science fairs and performing similar research on the Zika virus. After writing her paper, Sayalee shared her preprint through an online platform.
As a young researcher, Sayalee says she didn’t attempt peer review because she “assumed it would be really hard since [she] was a single author and it was a high school paper.” She also worried about the strictly computational nature of her work when approaching journals: “I was hearing other people who had solely computational work, and journals were hesitant to accept them because they didn’t have any experimental backings.”
Nevertheless, Sayalee’s paper - which got her placed among the top 300 scholars in the Regeneron Science Talent Search - has received 14 citations since publication. These citations include several literature reviews focusing on artificial intelligence and machine learning role at the forefront of combatting COVID-19.
Sayalee is optimistic about Jinso, a platform to connect high school students with research opportunities, to help researchers like herself. Jinso supports researchers throughout the entire research process, including allowing them to easily share completed papers.
Another difficulty that Sayalee faced was finding a research mentor, noting, “it’s been very hard to find mentors who are willing to work with you, especially as a high schooler.” Even beyond high school, finding an advisor can be difficult. “Professors are already reluctant to take in undergrads at their own institution,” she added.
A key function of Jinso’s network is connecting researchers with more experienced mentors. For researchers like herself, Sayalee believes that connecting with mentors “[is] the biggest [benefit]” to using Jinso. Having research mentors is the key to boosting the number of undergraduate and pre-university students engaged in research: “finding people who are willing to help you at such a young age is really helpful.”
This fall, Sayalee plans to begin her undergraduate studies at Harvard University, where she will concentrate in Chemical and Physical Biology. Staying involved in drug research is something that she strongly considers.
