论文标题
通过深层生成模型在受体结合位点上产生3D分子结构
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models
论文作者
论文摘要
作为微笑字符串和分子图的生成，深层生成模型已被越来越多地成功地生成二维分子。在这项工作中，我们首次描述了一个深层生成模型，该模型可以生成以三维（3D）结合袋为条件的3D分子结构。使用卷积神经网络，我们将原子密度网格编码为单独的受体和配体潜在空间。配体潜在空间是支持新分子采样的变化。解码器网络会产生以受体为条件的新型配体的原子密度。然后，离散原子适合这些连续密度以创建分子结构。我们表明，可以从由参考“种子”结构定义的变异潜在空间中轻松采样有效和独特的分子，并且生成的结构与结合位点具有合理的相互作用。随着结构在潜在空间中与种子结构相距更远，因此生成结构的新颖性增加，但预测的结合亲和力减少了。总体而言，我们证明了条件3D分子结构产生的可行性，并为也明确优化所需分子特性（例如高结合亲和力）的方法提供了一个起点。
Deep generative models have been applied with increasing success to the generation of two dimensional molecules as SMILES strings and molecular graphs. In this work we describe for the first time a deep generative model that can generate 3D molecular structures conditioned on a three-dimensional (3D) binding pocket. Using convolutional neural networks, we encode atomic density grids into separate receptor and ligand latent spaces. The ligand latent space is variational to support sampling of new molecules. A decoder network generates atomic densities of novel ligands conditioned on the receptor. Discrete atoms are then fit to these continuous densities to create molecular structures. We show that valid and unique molecules can be readily sampled from the variational latent space defined by a reference `seed' structure and generated structures have reasonable interactions with the binding site. As structures are sampled farther in latent space from the seed structure, the novelty of the generated structures increases, but the predicted binding affinity decreases. Overall, we demonstrate the feasibility of conditional 3D molecular structure generation and provide a starting point for methods that also explicitly optimize for desired molecular properties, such as high binding affinity.
