关于药物向相互作用和结合性亲缘关系预测的统一调查:模型,表示和挑战
Yike Wang1, Jingwei Lv1, Yan Xia1
1School of Computer Science and Technology, Hainan University, Haikou 570228, China.
Biotechnology advances
|February 14, 2026
概括
计算方法通过预测药物向相互作用 (DTI) 和结合亲和力 (DTA) 来加速药物发现. 本综述详细介绍了这些模型的演变,包括最近的大型预训练模型,并讨论了实验考虑.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 药物的发现和开发.
背景情况:
- 药物发现需要准确预测药物向相互作用 (DTI) 和结合亲和力 (DTA).
- 用于DTI和DTA预测的实验方法昂贵,缓慢,并且失败率很高.
- 计算预测方法提供了一种成本效益和高效的替代方案,改善了候选药物查.
研究的目的:
- 系统地审查计算药物向相互作用 (DTI) 和药物向结合亲和力 (DTA) 预测方面的研究进展.
- 突出大型预训练模型的出现和意义,作为DTI/DTA研究中一个独特的范式.
- 提供数据资源,计算模型和实地实验考虑的全面概述.
主要方法:
- 对DTI和DTA预测的计算方法现有文献的审查.
- 基于数据表示,模型演变 (从特征驱动到预训练模型) 和实验验证的方法的分类.
- 分析常用的资源,评估指标,冷启动问题处理和模型可解释性.
主要成果:
- 用于DTI/DTA预测的计算模型的技术演变的详细概述,包括基于序列,基于结构和大型预训练模型.
- 讨论数据和表示策略,计算问题定义和实验最佳实践.
- 确定该领域的关键挑战和未来研究方向.
结论:
- 计算方法,特别是大型预训练模型,对于高效和成功的药物发现至关重要.
- 对模型演变,数据处理和实验验证的全面理解对于推进DTI/DTA预测至关重要.
- 未来的研究应该专注于应对当前的挑战,并探索新的方法,以进一步提高预测的准确性和适用性.
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