Structure-based drug design (SBDD) aims to generate potential drugs that can
bind to a target protein and is greatly expedited by the aid of AI techniques
in generative models. However, a lack of systematic understanding persists due
to the diverse settings, complex implementation, difficult reproducibility, and
task singularity. Firstly, the absence of standardization can lead to unfair
comparisons and inconclusive insights. To address this dilemma, we propose
CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a
generative heterogeneous graph completion, analogous to fill-in-the-blank of
the 3D complex binding graph. By categorizing existing methods based on their
attributes, CBGBench facilitates a modular and extensible framework that
implements various cutting-edge methods. Secondly, a single task on \textit{de
novo} molecule generation can hardly reflect their capabilities. To broaden the
scope, we have adapted these models to a range of tasks essential in drug
design, which are considered sub-tasks within the graph fill-in-the-blank
tasks. These tasks include the generative designation of \textit{de novo}
molecules, linkers, fragments, scaffolds, and sidechains, all conditioned on
the structures of protein pockets. Our evaluations are conducted with fairness,
encompassing comprehensive perspectives on interaction, chemical properties,
geometry authenticity, and substructure validity. We further provide the
pre-trained versions of the state-of-the-art models and deep insights with
analysis from empirical studies. The codebase for CBGBench is publicly
accessible at https://github.com/Edapinenut/CBGBench.

CBGBench 是一个结构基药物设计的综合评估基准，使用 AI 技术中的生成模型，通过填补三维复合结合图的空白来统一任务，并广泛应用于药物设计的关键子任务，包括分子生成、连接物、片段、骨架和侧链等。

CBGBench：填补蛋白质 - 分子复合物结合图的空缺

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

De novo molecule generation allows the search for more drug-like hits across
a vast chemical space. However, lead optimization is still required, and the
process of optimizing molecular structures faces the challenge of balancing
structural novelty with pharmacological properties. This study introduces the
Deep Genetic Molecular Modification Algorithm (DGMM), which brings structure
modification to the level of medicinal chemists. A discrete variational
autoencoder (D-VAE) is used in DGMM to encode molecules as quantization code,
mol-gene, which incorporates deep learning into genetic algorithms for flexible
structural optimization. The mol-gene allows for the discovery of
pharmacologically similar but structurally distinct compounds, and reveals the
trade-offs of structural optimization in drug discovery. We demonstrate the
effectiveness of the DGMM in several applications.

通过 Deep Genetic Molecular Modification Algorithm (DGMM)，将结构修饰引入药物化学家的水平，该算法利用离散变分自动编码器 (D-VAE) 将分子编码为量化编码 mol-gene，将深度学习与遗传算法相结合，用于灵活结构优化，以发现药理学上相似但在结构上有所区别的化合物并揭示药物发现中结构优化的权衡。我们在几个应用中验证了 DGMM 的有效性。

分子 - 基因进化驱动的人类水平分子优化

Human-level molecular optimization driven by mol-gene evolution

Deep generative models that produce novel molecular structures have the
potential to facilitate chemical discovery. Diffusion models currently achieve
state of the art performance for 3D molecule generation. In this work, we
explore the use of flow matching, a recently proposed generative modeling
framework that generalizes diffusion models, for the task of de novo molecule
generation. Flow matching provides flexibility in model design; however, the
framework is predicated on the assumption of continuously-valued data. 3D de
novo molecule generation requires jointly sampling continuous and categorical
variables such as atom position and atom type. We extend the flow matching
framework to categorical data by constructing flows that are constrained to
exist on a continuous representation of categorical data known as the
probability simplex. We call this extension SimplexFlow. We explore the use of
SimplexFlow for de novo molecule generation. However, we find that, in
practice, a simpler approach that makes no accommodations for the categorical
nature of the data yields equivalent or superior performance. As a result of
these experiments, we present FlowMol, a flow matching model for 3D de novo
generative model that achieves improved performance over prior flow matching
methods, and we raise important questions about the design of prior
distributions for achieving strong performance in flow matching models. Code
and trained models for reproducing this work are available at
this https URL

在这项研究中，我们探索了用于生成化学分子的流匹配框架的扩展，该框架在 3D 分子生成方面取得了良好性能，并通过构建在概率简单形式上存在的流来对分类数据进行扩展，从而实现了改进的性能。