FSM-DDTR:使用变压器进行多目标De Novo药物设计的端到端反策略
Nelson R C Monteiro1, Tiago O Pereira1, Ana Catarina D Machado1
1University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
Computers in biology and medicine
|August 9, 2023
概括
这项研究引入了一种基于变压器的AI用于药物发现,产生具有高标选择性和可取药物特性的新分子. 人工智能优化药物候选物针对特定的生物标,提高药物设计效率.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 药物发现面临着由于多药理学和需要选择性化合物的挑战.
- 对于新药设计的现有深度学习方法往往忽视了诸如有效性和准选择性等关键性质.
- 药理学空间的多目标性质需要复杂的生成方法.
研究的目的:
- 开发一个基于变压器的多目标架构,用于生成具有增强分子性质和目标选择性的候选药物.
- 通过结合多目标优化来解决当前in silico药物设计方法的局限性.
- 为特定的生物标生成新,有效和选择性的化合物.
主要方法:
- 一个变压器解码器发电机被用来创建新的化合物在SMILES格式.
- 一个变压器编码器预测器估计了与目标的结合亲和力.
- 一个反循环和多目标优化策略排列分子和引导生成.
- 氨酸A2A受体 (AA2AR) 被用作验证的相关生物标.
主要成果:
- 基于变压器的发电机实现了97.38%的新率,表现优于基线.
- 生成的分子对AA2AR具有很高的结合亲和力,并且遵循Lipinski的五项规则 (99.36%).
- 多目标优化成功地将分子特性转移到所需的类似药物的特征,而无需先前的专业训练.
结论:
- 拟议的基于变压器的架构对于新药设计是有效的,产生具有改善药理特性和标选择性的新分子.
- 这种方法可以有效地探索化学空间,以确定潜在的毒品线索.
- 该研究验证了人工智能用于设计选择性和可合成的候选药物的使用.
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