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Abstract Antibody design is a time-consuming and expensive process that often requires extensive experimentation to identify the best candidates. To address this challenge, we propose an efficient and risk-aware antibody design framework that leverages protein language models (PLMs) and batch Bayesian optimization (BO).
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Accurate and timely destination prediction of subway passengers is of great significance in improving urban residents’ travel efficiency, alleviating urban traffic pressure, and recommending the proper location-based service.
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