情态动词
培训(气象学)
计算机科学
人工智能
训练集
机器学习
化学
地理
气象学
高分子化学
作者
Ying Qian,Xinyi Li,Jian Wu,Qian Zhang
标识
DOI:10.1016/j.compbiolchem.2024.108137
摘要
Compound-protein interaction (CPI) prediction plays a crucial role in drug discovery and drug repositioning. Early researchers relied on time-consuming and labor-intensive wet laboratory experiments. However, the advent of deep learning has significantly accelerated this progress. Most existing deep learning methods utilize deep neural networks to extract compound features from sequences and graphs, either separately or in combination. Our team's previous research has demonstrated that compound images contain valuable information that can be leveraged for CPI task. However, there is a scarcity of multimodal methods that effectively combine sequence and image representations of compounds in CPI. Currently, the use of text-image pairs for contrastive language-image pre-training is a popular approach in the multimodal field. Further research is needed to explore how the integration of sequence and image representations can enhance the accuracy of CPI task.
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