基于折叠的端到端化学药物设计与不确定性估计:在后GPT时代解决幻觉
Feisheng Zhong1, Rongcai Yue1, Jinxing Chen1,2
1Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350122, China.
Journal of medicinal chemistry
|March 8, 2025
概括
拉玛-格拉姆通过整合蛋白质折叠和图形表示来提高AI药物发现,以准确预测分子相互作用. 这种新的框架解决了传统模型的局限性,加速了新药的开发.
科学领域:
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
背景情况:
- 药物发现中的传统大语言模型 (LLM) 面临着幻觉问题.
- 了解蛋白质 - 配体相互作用对于识别有效的候选药物至关重要.
- 分子结构从根本上决定化学特性和生物活性.
研究的目的:
- 介绍Llama-Gram,一种用于化学药物发现的AI框架.
- 通过整合结构信息来解决LLM幻觉.
- 为了提高预测化合物-标相互作用的准确性和可靠性.
主要方法:
- 使用基于折叠的,端到端的框架.
- 整合蛋白质折叠嵌入 (ESMFold) 和基于图形的分子表示.
- 采用分组查询注意力和Gram层来增强预测能力.
- 纳入不确定性估计以捕捉相互作用的复杂性.
主要成果:
- 拉玛-格拉姆证明了与最先进的方法 (变压器CPI 2.0,图形DTA) 相比具有竞争力的性能.
- 该模型在预测化合物-标相互作用方面显示了改进的准确性.
- 该框架有效地捕捉了蛋白质 - 配体结合的结构复杂性.
结论:
- 拉玛-格拉姆为人工智能驱动的化学药物发现提供了可扩展和创新的方法.
- 蛋白质折叠信息的整合提高了预测可靠性.
- 这一进步有可能加速药物发现管道.
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