Structure-Based Drug Design (SBDD) focuses on generating valid ligands that strongly and specifically bind to a designated protein pocket. Several methods use machine learning for SBDD to generate these ligands in 3D space, conditioned on the structure of a desired protein pocket. Recently, diffusion models have shown success here by modeling the underlying distributions of atomic positions and types. While these methods are effective in considering the structural details of the protein pocket, they often fail to explicitly consider the binding affinity. Binding affinity characterizes how tightly the ligand binds to the protein pocket, and is measured by the change in free energy associated with the binding process. It is one of the most crucial metrics for benchmarking the effectiveness of the interaction between a ligand and protein pocket. To address this, we propose BADGER: Binding Affinity Diffusion Guidance with Enhanced Refinement. BADGER is a general guidance method to steer the diffusion sampling process towards improved protein-ligand binding, allowing us to adjust the distribution of the binding affinity between ligands and proteins. Our method is enabled by using a neural network (NN) to model the energy function, which is commonly approximated by AutoDock Vina (ADV). ADV's energy function is non-differentiable, and estimates the affinity based on the interactions between a ligand and target protein receptor. By using a NN as a differentiable energy function proxy, we utilize the gradient of our learned energy function as a guidance method on top of any trained diffusion model. We show that our method improves the binding affinity of generated ligands to their protein receptors by up to 60\%, significantly surpassing previous machine learning methods. We also show that our guidance method is flexible and can be easily applied to other diffusion-based SBDD frameworks.

翻译：基于结构的药物设计（SBDD）专注于生成能够与指定蛋白质口袋强效且特异性结合的有效配体。已有多种方法利用机器学习进行SBDD，在期望蛋白质口袋结构的条件下，于三维空间中生成这些配体。近年来，扩散模型通过建模原子位置与类型的底层分布，在此领域取得了成功。尽管这些方法能有效考虑蛋白质口袋的结构细节，却往往未能显式地考虑结合亲和力。结合亲和力表征了配体与蛋白质口袋结合的紧密程度，通常通过结合过程相关的自由能变化来度量，是评估配体与蛋白质口袋相互作用有效性的最关键指标之一。为解决此问题，我们提出了BADGER：结合亲和力扩散引导与增强优化。BADGER是一种通用引导方法，旨在引导扩散采样过程以改善蛋白质-配体结合，使我们能够调整配体与蛋白质间结合亲和力的分布。我们的方法通过使用神经网络（NN）对能量函数进行建模来实现，该能量函数通常由AutoDock Vina（ADV）近似。ADV的能量函数不可微，其基于配体与目标蛋白受体间的相互作用来估算亲和力。通过使用神经网络作为可微能量函数代理，我们将学习到的能量函数的梯度作为引导方法，应用于任何已训练的扩散模型之上。实验表明，我们的方法将生成配体与其蛋白受体的结合亲和力提升了高达60%，显著超越了以往的机器学习方法。我们还证明该引导方法具有灵活性，可轻松应用于其他基于扩散的SBDD框架。