We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html

中文翻译：

---

使用内坐标和生成定向图卷积神经网络高效生成顺应器系综

我们提出了一种基于神经网络的高通量分子构象异构体生成算法。化学图卷积网络经过训练，从二维 (2D) 化学拓扑开始，预测内部坐标表示（键长、键和扭转角）中的低能构象异构体。生成神经网络 (NN) 架构对扭转空间进行去噪，生成具有与扭转能量分布良好相关的群体的构象异构体系综。应用基于短力场的能量最小化来完善最终的构象异构体。该算法的所有计算密集型阶段均经过 GPU 优化。该程序（称为 GINGER）以 PDB 中生物活性三维 (3D) 构象异构体的常用测试集为基准。我们在适合千兆级化合物库处理的构象异构体回收率和吞吐率方面展示了极具竞争力的结果。一个 Web 服务器，允许 GINGER 生成交互式一致体集成，并可在 https://www.molsoft.com/gingerdemo.html 免费获取它们