用三维药效团生成可合成分子，加速药物设计
SynthFormer: Equivariant Pharmacophore-based Generation of Synthesizable Molecules for Ligand-Based Drug Design
- 结合药效团与3D结构信息，生成可合成分子
- 在多个靶点上成功设计活性分子并优化性质
- 适合需要快速构建可合成候选药物的研究者
药物发现过程复杂且耗时耗资。尽管许多生成模型旨在加速这一进程，但很少能产出可合成的分子；而专注于合成性的模型又忽略了药物设计中至关重要的三维信息。我们提出 SynthFormer，一种新型机器学习模型，通过引入三维信息和药效团作为输入，生成完整可合成的分子，以合成树的形式表示。该模型采用3D等变图神经网络编码药效团，再通过基于Transformer的合成感知解码机制，将合成树作为令牌序列逐步构建。这是首个结合药效团驱动与可合成性约束的方法，可用于基于药效团设计活性分子、扩展命中分子的局部可合成化学空间，并优化其性质。我们在多种挑战性任务中验证了其有效性，包括针对多个蛋白质设计活性化合物、进行命中分子扩展及优化分子属性。
原文摘要 · Abstract (English)
Drug discovery is a complex, resource-intensive process requiring significant time and cost to bring new medicines to patients. Many generative models aim to accelerate drug discovery, but few produce synthetically accessible molecules. Conversely, synthesis-focused models do not leverage the 3D information crucial for effective drug design. We introduce SynthFormer, a novel machine learning model that generates fully synthesizable molecules, structured as synthetic trees, by introducing both 3D information and pharmacophores as input. SynthFormer features a 3D equivariant graph neural network to encode pharmacophores, followed by a Transformer-based synthesis-aware decoding mechanism for constructing synthetic trees as a sequence of tokens. It is a first-of-its-kind approach that could provide capabilities for designing active molecules based on pharmacophores, exploring the local synthesizable chemical space around hit molecules and optimizing their properties. We demonstrate its effectiveness through various challenging tasks, including designing active compounds for a range of proteins, performing hit expansion and optimizing molecular properties.
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