使用人工智能预测分子的幻觉潜力
Fabio Urbina1, Thane Jones1, Joshua S Harris1
1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
ACS chemical neuroscience
|August 2, 2024
概括
研究人员开发了人工智能模型来预测迷幻效应,旨在为像阿片类药物使用障碍这样的心理健康治疗设计更安全的"心理塑原体". 这些模型有助于识别具有治疗潜力的但没有幻觉性质的化合物.
科学领域:
- 神经科学和药理学 神经科学和药理学
- 计算化学和化学信息学
背景情况:
- 被称为"心理塑原剂"的迷幻类药物通过诱导神经可塑性,显示出治疗阿片类药物使用障碍等疾病的前景.
- 预测幻觉潜在的挑战是由于各种机制,包括G蛋白结合受体 (GPCR) 5HT2A相互作用和复杂的多药学.
研究的目的:
- 开发人工智能 (AI) 工具,特别是机器学习分类模型,以预测分子的迷幻效应.
- 设计具有治疗功效但缺乏体内幻觉潜力的新型心理塑料原体.
主要方法:
- 利用机器学习分类模型,包括支持矢量分类 (SVC) 和随机森林,并进行嵌套的五倍交叉验证.
- 在体外 (PsychLight) 和体内 (Shulgin的书籍) 人类数据上训练有素的模型,结合ECFP6和静电描述器与合规预测器.
- 通过预测已知的5HT2A激动剂并使用小鼠头部抽数据评估它们的幻觉潜力来验证模型.
主要成果:
- 在曲线 (AUC) 下取得的区域为0.74 (PsychLight in vitro) 和0.72 (Shulgin人体数据),用于预测迷幻效应.
- 模型显示已知5HT2A激动剂的高预测精度,AUC为0.97 (PsychLight) 和0.71 (舒尔金数据).
结论:
- 人工智能驱动的预测模型在评估心理塑原体候选人的幻觉潜力方面是有效的.
- 这些工具对于可靠设计新的治疗分子至关重要,这些分子将神经可塑性效应与迷幻体验分开.
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