The machine assisted product screening (MAPS) project is dedicated to leveraging the collective biological and chemical knowledge to bridge the gap between synthetic biology and synthetic chemistry. Its primary aim is to explore bio-derived monomers for creating sustainable polymers with customizable properties. With the pressing need to combat pollution caused by petroleum-based plastics, MAPS will provide a vital platform for researchers to discover and develop promising alternatives.

A few pre-existing machine learning (ML) tools have excelled in predicting chemical reactions based on their reactivities. However, when it comes to polymerization reactions involving radicals and other complex processes, these conventional tools often fall short, predicting unrealistic outcomes. This limitation arises from the lack of training data specific to polymer synthesis.

To address this challenge,  MAPS is on a mission to compile a comprehensive database of polymerizability tailored to natural product molecules. Establishing the MAPS database is a big challenge due to the absence of a pre-existing database in this domain. To address this obstacle, we are actively seeking synthetic expertise from experts to develop a targeted ML algorithm for predicting polymerizability. But we can't do it alone...

By actively participating in the MAPS project, you're not just contributing data – you're driving sustainable materials innovation in polymer research. Join us in shaping a sustainable future and innovating bio-friendly materials for polymer community!

Are you ready to join us in enriching our MAPS database?

We’re excited to offer both the MAPS web portal and the MAPS mobile app (available on the Apple App Store – “BioPACIFIC MIP MAPS”) so you can participate in whichever way is most convenient for you!

After logging in, simply choose the “Polymerization Motif” you feel most comfortable rating and start exploring.

Think of it like rating a restaurant on Yelp - but for molecules! You’ll be rating molecular (monomer) quality and functional group quality using a 5-star scale, ranging from:

to

If a molecule isn’t perfect as-is but could become promising with small modifications, feel free to rate it as 3 – “Potentially Works”. This helps highlight molecules that may have hidden potential.

For Web Portal Users:

After finishing the ratings on a page, be sure to click “Save & Next Page” so your responses are recorded.

Most importantly — have fun exploring new molecules!

If you run into any challenges while rating molecules or have questions along the way, please don’t hesitate to reach out to Saejin (saejinoh@ucsb.edu).