用三维分子交互特征生成新药分子，提升设计精准度。
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
- 构建联合扩散模型，同步生成分子结构与三维相互作用特征。
- 在天然产物配体跳跃等任务中生成高相似性新分子。
- 适合需精确三维互作设计的药物研发人员使用。
通过精确调控分子与环境的三维分子间相互作用，可奠定化学设计基础。在基于配体的药物设计中，通常通过形状、静电和药效团相似性评分函数虚拟筛选化学库，以识别已知活性分子的生物等排体。我们提出假设：学习三维分子结构及其相互作用特征联合分布的生成模型，可能实现更具备三维相互作用感知能力的化学设计。为此，我们专门设计了ShEPhERD，一种基于SE(3)-等变的扩散模型，可同步对三维分子图及其形状、静电势面和（方向性）药效团表示进行去噪与扩散，从高斯噪声中生成或重构。受传统配体发现启发，我们构建了三维相似性评分函数，用于评估ShEPhERD在条件生成具有特定相互作用特征的新分子方面的能力。通过自然产物配体跳跃、蛋白无关的活性先导分子多样化及生物等排片段融合等示范性药物设计任务，展示了其潜在影响力。
原文摘要 · Abstract (English)
Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD's ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD's potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging.
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