通过将基于基和基于结构的药物发现策略与转移和深度学习技术相结合,提高阿片类生物活性预测
bioRxiv : the preprint server for biology
|August 23, 2023
概括
这项研究使用转移学习和深度学习来改善阿片类药物活性的预测,旨在找到对当前药物更安全的替代品,并打击阿片类药物流行病.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 阿片类药物流行需要开发更安全的止痛药,降低成潜力.
- 机器学习,特别是深度学习,显示出加速药物发现的希望,但在有限的训练数据下面临挑战.
- 片受体 (OR) 亚型选择性是开发向治疗的关键.
研究的目的:
- 调查转移学习的有效性,以建立强大的深度学习模型来预测阿片类配体生物活性.
- 为了提高使用组合分子描述器的阿片类受体 (OR) 亚型特定生物活性的预测性能.
- 促进新型化学探针和改进的阿片类药物治疗方法的识别.
主要方法:
- 利用转移学习技术,利用整个阿片类受体 (OR) 子家族的数据.
- 用于模型训练的基于连接体和基于结构的组合分子描述符.
- 开发了深度学习模型,用于预测单个OR亚型的生物活性.
主要成果:
- 证明了转移学习在克服深度学习模型数据限制方面的有效性.
- 在OR亚型中实现了对OR亚型中阿片类配体的增强生物活性预测准确性.
- 成功构建了强大的深度学习模型,以改善药物发现.
结论:
- 这项研究展示了一种强大的人工智能驱动的方法,用于识别具有特定生物活性的新型阿片类配体.
- 这些发现可以加速发现改进的阿片类药物治疗方法和用于受体研究的化学探针.
- 开发的模型有可能通过更安全的药物开发来推动对抗阿片类药物危机的斗争.
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