使用生成性AI模型和基于结构的药物设计加速发现碳酸盐Cbl-b抑制剂
Taylor R Quinn1, Kathryn A Giblin2, Clare Thomson2
1Early TDE Discovery, Oncology R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States.
Journal of medicinal chemistry
|August 12, 2024
概括
研究人员使用生成人工智能和基于结构的药物设计来发现新的Casitas B-淋巴瘤原基因-b (Cbl-b) 抑制剂. 这加速了强大的碳酸盐Cbl-b抑制剂的鉴定,推动了T细胞调节研究.
科学领域:
- 免疫学 免疫学 免疫学
- 药用化学 医学化学
- 人工智能在药物发现中的作用
背景情况:
- 卡西塔斯B淋巴瘤原瘤基因-b (Cbl-b) 是一个关键的E3酶,调节T细胞,NK细胞和B细胞激活.
- Cbl-b作为负调节剂,使其成为调节免疫反应的目标.
研究的目的:
- 使用生成AI和基于结构的药物设计的组合,发现Cbl-b的新型抑制剂.
- 为了加速Cbl-b抑制剂的药物发现过程.
主要方法:
- 整合REINVENT生成AI引擎与基于药物化学结构的设计.
- 在设计-制造-测试-分析周期内,基于体结构的代药物设计.
- 利用基于物理的亲和力预测和机器学习的DMPK模型来指导设计.
主要成果:
- 成功发现了一系列强大的碳酸盐Cbl-b抑制剂.
- 通过集成的人工智能和基于结构的设计方法来证明加速发现.
- 验证了 in silico预测模型在指导合成选择中的有效性.
结论:
- 结合生成人工智能和基于结构的设计方法,显著加快了新药候选药物的发现.
- 这一策略有效地确定了强大的Cbl-b抑制剂,突出了其对未来药物发现工作的潜力.
- 优化设计阶段提高了识别向分子抑制剂的效率和成功率.
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