Discovering the right journal with Molecular Connections’ Journal Recommendation Service

Discovering the right journal with Molecular Connections’ Journal Recommendation Service

IFIS is a society publisher committed to helping the global food community find and explore trustworthy information directly related to the sciences of food and health.


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Discovering the right journal for submission of your manuscript is the most vital aspect of academic research and sometimes it can be a daunting task. To have the desired impact of your research, it is important to find the journal that attracts the relevant audience and enables one to showcase their work. A wrong selection not only leads to an increased risk of rejection, but for the author and the journal’s editorial team, it is a waste of time and resources. IFIS was looking for a tool that will help authors in this essential step of publication process, avoid submission to predatory journals, and also enable the publisher increase its productivity. The client sought a partner who could provide a global, publisher-neutral service that matches the manuscript abstract to the journal scope using artificial intelligence (AI) and machine learning techniques.


The MC team designed a platform for IFIS wherein users can use keywords or journal titles to search and browse the FSTA database of trusted journals, all of which have passed IFIS’ rigorous journal assessment policy. Further, users can narrow down the results by filtering by the criteria that matter to them, such as open access options and Impact Factor.

Utilizing Molecular Connections’ robust state-of-the-art deep learning algorithms, the team developed the only journal finder tool designed especially for researchers in the sciences of food and health. The heuristics related to the journals recommended also contributes to the suggestion; including, but not limited to journal quality metrics, publisher, and accessibility.

The team incorporated various contextual vectorizations/representations such as word and sentence embedding in conjugation; custom inserted generalized layers across the model; bi-directional transformers are used as an attempt to construct and learn dependency grammars; and a Siamese encoder-decoder is used in conjugation with a BERT: a deep bi-directional transformer to handle large corpus and ensure that it works efficiently with live inputs.

To ensure optimum performance, the team also employed few techniques such as Quantization and Approximate Nearest Neighbours (ANN) search.


Molecular Connections helped the client achieve its business objective. The tool needs bare minimum re-training time and content to include any new journals. The easy-to-use interface enabled users to filter the recommendations based on APC, research areas, time to publish, impact factors, thus, resulting in increased traffic by 30 percent. Users were able to identify well-matched journals based on their title, abstract, and the interested research areas. Using MC algorithms and AI-based recommendation, one can even fine-tune the title and abstract to identify the perfect journal.

The platform covers over 1000 journals from publishers all over the world—food focused as well as interdisciplinary and comes with filters for key criteria such as open access type and publishers.

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