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3Rings: A fast and accurate method for ring system identification and deep generation of drug-like cyclic compounds

3Rings: A fast and accurate method for ring system identification and deep generation of drug-like cyclic compounds

中文摘要英文摘要

ontinuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in 3 different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, fused and bridged rings. Based on this, three deep generative models, namely VAE, AAE, and CharRNN, were trained and used to construct two datasets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged datasets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like dataset against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures were exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.

ontinuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in 3 different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, fused and bridged rings. Based on this, three deep generative models, namely VAE, AAE, and CharRNN, were trained and used to construct two datasets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged datasets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like dataset against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures were exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.

10.12074/202402.00090V1

药学生物科学研究方法、生物科学研究技术制药化学工业

3Ringsring system identificationdeep generationcyclic compounds

3Ringsring system identificationdeep generationcyclic compounds

.3Rings: A fast and accurate method for ring system identification and deep generation of drug-like cyclic compounds[EB/OL].(2024-02-06)[2025-06-27].https://chinaxiv.org/abs/202402.00090.点此复制

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