NEBULA：基于潜在表示的神经经验贝叶斯方法，用于高效且可控的分子库设计
NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries
AI总结：
NEBULA首次在向量量化变分自编码器的潜在空间中执行神经经验贝叶斯采样，实现围绕种子化合物高效生成大型分子库，速度比现有方法快近一个数量级且泛化性更好。
摘要：
我们提出NEBULA，这是首个潜在3D生成模型，用于围绕感兴趣的种子化合物可扩展地生成大型分子库。此类分子库对科学发现至关重要，但高效生成大量高质量样本仍具挑战性。基于3D体素的方法最近在从随机噪声从头生成高质量样本方面展现出巨大潜力（Pinheiro et al., 2023）。然而，在3D体素空间中进行采样计算成本高昂，用于分子库生成时速度极慢。在此，我们改为在向量量化变分自编码器的学习潜在空间中执行神经经验贝叶斯采样（Saremi & Hyvarinen, 2019）。NEBULA生成大型分子库的速度比现有方法快近一个数量级，且不牺牲样本质量。此外，NEBULA对未见过的类药分子具有更好的泛化能力，这一点在两个公开数据集和多个近期获批药物上得到了验证。我们期望本文方法能够极大地推动基于机器学习的药物发现。代码可在 https://github.com/prescient-design/nebula 获取。
英文摘要：
We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebula
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
