Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model. SCIENTIFIC CONTRIBUTION: This study introduces GENiPPI, a protein-protein interaction (PPI) interface-aware molecular generative framework. The framework first employs Graph Attention Networks to capture atomic-level interaction features at the protein complex interface. Subsequently, Convolutional Neural Networks extract compound representations in voxel and electron density spaces. These features are integrated into a Conditional Wasserstein Generative Adversarial Network, which trains the model to generate compound representations targeting PPI interfaces. GENiPPI effectively captures the relationship between PPI interfaces and active/inactive compounds. Furthermore, in fewshot molecular generation, GENiPPI successfully generates compounds comparable to known disruptors. GENiPPI provides an efficient tool for structure-based design of PPI modulators.

Jianmin Wang

Jiashun Mao

Chunyan Li

Hongxin Xiang

Xun Wang

Shuang Wang

Zixu Wang

Yangyang Chen

Yuquan Li

Kyoung Tai No

Tao Song

Xiangxiang Zeng

Journal of Cheminformatics

JOURNAL OF CHEMINFORMATICS 是一本开放获取期刊，发表化学信息学和分子建模各个方面的原始同行评议研究。覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模化学结构表示及其在结构中的应用、化学物质和化学反应数据库的子结构和相似性搜索计算机和分子图形、计算机辅助分子设计、专家系统、QSAR 和数据挖掘技术。

The Journal of Cheminformatics is an open-access journal dedicated to original peer-reviewed research in all aspects of cheminformatics and molecular modeling. Its scope includes, but is not limited to: chemical information systems, software and databases, molecular modeling of chemical structure representations and their applications, substructure and similarity searching in chemical substance and reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR (Quantitative Structure-Activity Relationship), and data mining techniques.

《Journal of Cheminformatics》是一本开放获取的期刊，专注于化学信息学和分子建模领域的原创性同行评审研究。期刊的覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模中的化学结构表示及其应用、化学物质和化学反应数据库的子结构和相似性搜索、计算机和分子图形、计算机辅助分子设计、专家系统、QSAR（定量构效关系）和数据挖掘技术。

J Cheminform

Interface-aware molecular generative framework for protein–protein interaction modulators

面向蛋白质-蛋白质相互作用调节剂的界面感知分子生成框架

Category	Quartile
化学	2
化学, 化学综合	2
化学, 计算机信息系统	2
化学, 计算机跨学科应用	3

Interface-aware molecular generative framework for protein–protein interaction modulators

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Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	1