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Recent advancements in machine learning and computational chemistry are significantly enhancing various fields of scientific research. A novel active learning workflow combining machine learning and physics-based computations has been developed for energy-based antibody optimization, improving screening efficiency. Additionally, systemic CD8+ T cell effector signatures have been identified as predictors of lung cancer immunotherapy prognosis. In the realm of enzyme design, coupling DNA manufacturing settings to fine-tuned protein language models has shown better sampling of predicted fitness landscapes. Furthermore, machine learning techniques are being utilized to optimize enzyme cascades, predict fluorescence to singlet oxygen generation quantum yield ratios for BODIPY dyes, and identify multi-omics features of immunogenic cell death in gastric cancer. Improved survival for patients with lung cancer treated with perioperative immunotherapy has also been reported. These advancements highlight the growing impact of machine learning in enhancing the accuracy and efficiency of scientific research.