在药物研发的前沿，预测蛋白质与配体的结合亲和力是筛选和优化药物的决定性阶段。然而，目前的预测方法往往忽视了分子表面信息在蛋白质-配体相互作用中的重要性。近日，厦门大学的研究团队成功开发了一种创新的多模态特征提取（MFE）框架，这一突破性进展填补了现有预测方法的空白，显著提升了蛋白质-配体结合亲和力的预测精度。
MFE框架首次将蛋白质表面、三维结构以及序列信息整合在一起，通过交叉注意机制实现了不同模态特征的有效对齐。这一集成方法在预测蛋白质-配体结合亲和力方面展现出卓越性能，其预测准确度超越了现有技术。研究团队的消融分析进一步证实了蛋白质表面信息和多模态特征对齐的必要性和有效性，为未来药物研发提供了更精准的预测工具。
这一研究成果以“Surface-based multimodal protein–ligand binding affinity prediction”为题，在《Bioinformatics》期刊上发表，为生物信息学和药物发现领域带来了新的突破。通过将分子表面信息纳入预测模型，MFE框架不仅提高了预测的准确性，还为理解蛋白质-配体相互作用提供了更全面的视角。
厦门大学的研究团队展示了跨学科合作在推动科学进步方面的巨大潜力，MFE框架的成功开发不仅对药物研发领域具有重要意义，也为其他生物信息学应用提供了新的思路。随着该技术的进一步推广和应用，有望加速新药的研发进程，为全球医药健康产业带来变革性的影响。
这项研究不仅体现了厦门大学在生物信息学领域的学术实力，也展示了中国科研机构在前沿科学领域取得的国际影响力。未来，随着多模态特征提取技术的持续优化和应用拓展，我们有理由期待其在药物研发、精准医疗等多个领域发挥更为广泛和深远的作用。
英语如下：
News Title: “Xiamen University Pioneers Innovative AI Approach: Significantly Boosts Accuracy in Predicting Protein-Ligand Affinity”
Keywords: Multimodal prediction, Protein surface information, Advanced performance
News Content: At the forefront of drug development, predicting the affinity of protein-ligand binding is a critical step in screening and optimizing drugs. However, current prediction methods often overlook the significance of molecular surface information in protein-ligand interactions. Recently, a research team from Xiamen University successfully developed an innovative multimodal feature extraction (MFE) framework, a groundbreaking advancement that fills the gap in existing prediction methods, significantly enhancing the accuracy of predicting protein-ligand binding affinity.
The MFE framework integrates information from protein surfaces, three-dimensional structures, and sequences through a cross-attention mechanism, effectively aligning features from different modalities. This integrated approach showcases outstanding performance in predicting protein-ligand binding affinity, surpassing existing technologies in prediction accuracy. The team’s ablation analysis further validates the necessity and effectiveness of incorporating protein surface information and multimodal feature alignment, providing a more precise predictive tool for future drug development.
This research, titled “Surface-based multimodal protein-ligand binding affinity prediction,” was published in the Bioinformatics journal, marking a new breakthrough in the fields of bioinformatics and drug discovery. By incorporating molecular surface information into the prediction model, the MFE framework not only improves prediction accuracy but also offers a more comprehensive perspective on understanding protein-ligand interactions.
The Xiamen University research team demonstrated the potential of interdisciplinary collaboration in driving scientific progress, with the successful development of the MFE framework having significant importance for the drug development field and providing new avenues for other bioinformatics applications. As the technology is further promoted and applied, it is anticipated to accelerate the development of new drugs and bring transformative impacts on the global pharmaceutical and healthcare industry.
This study not only highlights Xiamen University’s academic prowess in bioinformatics but also showcases the international influence of Chinese research institutions in cutting-edge scientific fields. Looking ahead, with the continuous optimization and application expansion of multimodal feature extraction techniques, we can expect them to play a more extensive and profound role in drug development, precision medicine, and other fields.
The research underscores the academic strength of Xiamen University in the field of bioinformatics and the international impact of Chinese research institutions in frontier scientific areas. As this technology continues to evolve and be applied, it is reasonable to anticipate its transformative role in various fields, from drug development to precision medicine, and beyond.
【来源】https://www.jiqizhixin.com/articles/2024-07-16-7
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