两级扩散与多性质优化：一种生成具有理想性质分子的新方法
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties
AI总结：
针对现有分子生成模型仅关注基础性质或单性质优化、实用性不足的问题，提出两级扩散加电子效应碎片化、能量引导加多目标优化的扩散新方法，在多基准上多项指标优于现有最优模型。
摘要：
在过去十年中，人工智能驱动的药物设计与发现一直是热门研究方向，其中一个重要分支是利用生成模型进行分子生成，涵盖了从基于GAN（生成对抗网络）的模型、基于VAE（变分自编码器）的模型到最新的基于扩散的模型。然而，现有大多数模型仅追求生成分子的有效性、唯一性等基础性质，少数模型进一步对单一重要分子性质（如QED或PlogP）进行显式优化，这导致大多数生成的分子在实际中实用性极低。\n本文提出一种生成具有理想性质分子的新方法，通过多项创新设计扩展了扩散模型框架，其创新性体现在两方面。一方面，考虑到分子结构复杂多样，且分子性质通常由某些子结构（如药效团）决定，我们提出在分子和分子片段两个结构层级分别执行扩散，由此为反向扩散过程得到混合高斯分布。为获取理想的分子片段，我们开发了一种基于电子效应的新型碎片化方法。另一方面，我们引入两种在扩散模型框架下显式优化多种分子性质的方式：首先，由于潜在药物分子必须具备化学有效性，我们通过能量引导函数优化分子有效性；其次，由于潜在药物分子需在多种性质上表现理想，我们采用多目标机制同时优化多种分子性质。\n在QM9和ZINC250k两个基准数据集上开展的大量实验表明，与当前SOTA（最优水平）模型生成的分子相比，本文方法生成的分子在有效性、唯一性、新颖性、Fréchet ChemNet距离（FCD）、QED以及PlogP指标上表现更优。
英文摘要：
In the past decade, Artificial Intelligence driven drug design and discovery has been a hot research topic, where an important branch is molecule generation by generative models, from GAN-based models and VAE-based models to the latest diffusion-based models. However, most existing models pursue only the basic properties like validity and uniqueness of the generated molecules, a few go further to explicitly optimize one single important molecular property (e.g. QED or PlogP), which makes most generated molecules little usefulness in practice. In this paper, we present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. The novelty is two-fold. On the one hand, considering that the structures of molecules are complex and diverse, and molecular properties are usually determined by some substructures (e.g. pharmacophores), we propose to perform diffusion on two structural levels: molecules and molecular fragments respectively, with which a mixed Gaussian distribution is obtained for the reverse diffusion process. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. On the other hand, we introduce two ways to explicitly optimize multiple molecular properties under the diffusion model framework. First, as potential drug molecules must be chemically valid, we optimize molecular validity by an energy-guidance function. Second, since potential drug molecules should be desirable in various properties, we employ a multi-objective mechanism to optimize multiple molecular properties simultaneously. Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fréchet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
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。
