Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

Matthew Tamasi, Roshan A. Patel, Carlos H. Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael E. Webb et al.

Advanced Materials 2022 article cited by 177

Source Open-access PDF More in Engineering & Materials

importance 4/10 partially solved Machine Learning in Materials Science polymer materials stage: research

Problem

Designing synthetic copolymers that stabilize proteins is difficult because the chemical and composition space is too vast for rational design.

Why it matters

Protein-stabilizing polymers expand the use of enzymes in medicine, industry and commerce.

What the AI did

Active machine learning iteratively guided an automated robotic synthesis and characterization platform to identify copolymer chemistries that stabilize proteins.

Results

Copolymers identified that preserved or enhanced activity of three chemically distinct enzymes after thermal denaturation; systematic screening had mixed success; no numbers stated.

Impact

Peer-reviewed Advanced Materials paper with 177 citations demonstrating active learning finds effective copolymers where screening fails.

Record

Venue	Advanced Materials
Published	2022
Work type	article
Authors	Matthew Tamasi, Roshan A. Patel, Carlos H. Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael E. Webb et al.
Citations	177 (OpenAlex, at import time)
References	62
Model or method	active machine learning (specific model not stated in source)
Organization	not stated in source
Primary source	https://doi.org/10.1002/adma.202201809
Open access	hybrid
Keywords	active learning, robotic platform, copolymers, enzyme stabilization, polymer-protein hybrids, automation

Importance rating

4 / 10

real but narrower advance

Active learning finds protein-stabilizing copolymers for three enzymes; useful for medicinal/industrial applications but a real, narrower materials-design advance with no stated scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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