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, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated
by current SOTA models.

本文介绍了一种生成具有理想属性的分子的新方法，该方法通过在分子和分子片段上执行扩散来获取混合高斯分布，并使用新颖的基于电子效应的分解方法优化多个分子属性，从而在人工智能驱动的药物设计和发现方面具有重要的应用前景。

两层扩散和多属性优化：一种生成具有理想特性分子的新方法

Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel  Approach to Generating Molecules with Desirable Properties

Recent advances in text-guided image synthesis has dramatically changed how
creative professionals generate artistic and aesthetically pleasing visual
assets. To fully support such creative endeavors, the process should possess
the ability to: 1) iteratively edit the generations and 2) control the spatial
reach of desired changes (global, local or anything in between). We formalize
this pragmatic problem setting as Iterative Multi-granular Editing. While there
has been substantial progress with diffusion-based models for image synthesis
and editing, they are all one shot (i.e., no iterative editing capabilities)
and do not naturally yield multi-granular control (i.e., covering the full
spectrum of local-to-global edits). To overcome these drawbacks, we propose
EMILIE: Iterative Multi-granular Image Editor. EMILIE introduces a novel latent
iteration strategy, which re-purposes a pre-trained diffusion model to
facilitate iterative editing. This is complemented by a gradient control
operation for multi-granular control. We introduce a new benchmark dataset to
evaluate our newly proposed setting. We conduct exhaustive quantitatively and
qualitatively evaluation against recent state-of-the-art approaches adapted to
our task, to being out the mettle of EMILIE. We hope our work would attract
attention to this newly identified, pragmatic problem setting.

最近关于文本引导图像合成的研究取得了巨大的进展，改变了创意专业人员生成艺术性和美观性视觉资产的方式。为了全面支持此类创意努力，该过程应具备迭代编辑生成物和控制所需更改的空间范围（全局、局部或介于两者之间）的能力。为了克服当前图像合成和编辑中的一些不足，我们提出了 EMILIE：迭代多粒度图像编辑器。EMILIE 引入了一种新的潜在迭代策略，该策略重构了预训练扩散模型以实现迭代编辑。此外，我们还引入了渐变控制操作以实现多粒度控制。我们还引入了一个新的基准数据集来评估我们新提出的设置。我们对近期最先进的方法进行了详尽的定量和定性评估，并展示了 EMILIE 的能力水平。我们希望我们的工作能够引起对这个新确定的实际问题设置的关注。