Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph.
Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph.
复制标题
通过知识图使用基于强化学习的图神经网络生成目标蛋白 (SARS-CoV-2) 的分子。
DOI：
10.1007/978-3-030-90870-6_13
复制
发表时间：
2023
影响因子：
2.3
通讯作者：
Ranjan A
中科院分区：
文献类型：
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作者：
Ranjan A
近年来，随着操作语义和相关逻辑的发展，C11程序的演绎验证技术取得了显著进展，用于越来越大的C11片段。然而，这些语义和逻辑是在受限的环境中开发的，以避免稀薄空气读取问题。在本文中，我们提出了一种操作语义，该语义利用了最近开发的基于指称事件结构的语义引起的线程内部分顺序（称为语义依赖）。我们证明了我们的操作语义相对于指称语义是健全和完备的。我们提出了一个相关的逻辑，它推广了RC11（修复的C11）的最新Owicki-Gries框架，并通过几个示例证明演示了该逻辑的使用。
Deductive verification techniques for C11 programs have advanced significantly in recent years with the development of operational semantics and associated logics for increasingly large fragments of C11. However, these semantics and logics have been developed in a restricted setting to avoid thethin-air-readproblem. In this paper, we propose an operational semantics that leverages an intra-thread partial order (calledsemantic dependencies) induced by a recently developed denotational event-structure-based semantics. We prove that our operational semantics is sound and complete with respect to the denotational semantics. We present an associated logic that generalises a recent Owicki-Gries framework for RC11 (repaired C11), and demonstrate the use of this logic over several example proofs.