Virtual High-Throughput Screening of Vapor-Deposited Amphiphilic Polymers for Inhibiting Biofilm Formation

Amphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry-composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a "pooled" dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best-performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model-selected AP chemistries and compositions for validation at solid-liquid interface (often used in conventional antifouling studies) as well as the air-liquid-solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best-performing AP for biofilm inhibition at the triple interface.