Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Dou Nie, Huifeng Zhao, Odin Zhang, Gaoqi Weng, Hui Zhang, Jieyu Jin, Haitao Lin, Yufei Huang, Liwei Liu, Dan Li, Tingjun Hou, Yu Kang
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引用次数: 0

Abstract

Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings. In this work, we proposed Durian, an evaluation framework for structure-based 3D molecular generation that incorporates protein-ligand data with experimental affinity and a comprehensive array of physicochemical and geometric metrics. The benchmark tasks encompass assessing the capability of models to reproduce the property distribution of training sets, generate molecules with rational distributions of drug-related properties, and exhibit potential high affinity toward given targets. Binding affinities were evaluated using three independent docking methods (QuickVina2, Surflex and Gnina) with both "Dock" and "Score" modes to reduce false positives arising from conformational searches or scoring functions. Specifically, we applied Durian to six 3D molecular generation methods: LiGAN, Pocket2Mol, DiffSBDD, SBDD, GraphBP, and SurfGen. While most methods demonstrated the ability to generate drug-like small molecules with reasonable physicochemical properties, they exhibited varying degrees of limitations in balancing novelty, structural rationality, and synthetic accessibility, thereby constraining their practical applications in drug discovery. Based on a total of 17 metrics, Durian highlights the importance of multiobjective optimization in 3D molecular generation methods. For instance, SurfGen and SBDD showed relatively comprehensive performance but could benefit from further improvements in molecular conformational rationality. Our evaluation framework is expected to provide meaningful guidance for the selection, optimization, and application of 3D generative models in practical drug design tasks.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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