Tao Lin, Zhu Feng, Qin Chu, Zhang Cheng, Chen Shangying, Zhang Peng, Zhang Cunlong, Tan Chunyan, Gao Chunmei, Chen Zhe, Jiang Yuyang, Chen Yu Zong
1] Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China [2] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [3] NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456.
1] Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China [2] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [3] Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, P. R. China.
Sci Rep. 2015 Mar 20;5:9325. doi: 10.1038/srep09325.
Some natural product leads of drugs (NPLDs) have been found to congregate in the chemical space. The extent, detailed patterns, and mechanisms of this congregation phenomenon have not been fully investigated and their usefulness for NPLD discovery needs to be more extensively tested. In this work, we generated and evaluated the distribution patterns of 442 NPLDs of 749 pre-2013 approved and 263 clinical trial small molecule drugs in the chemical space represented by the molecular scaffold and fingerprint trees of 137,836 non-redundant natural products. In the molecular scaffold trees, 62.7% approved and 37.4% clinical trial NPLDs congregate in 62 drug-productive scaffolds/scaffold-branches. In the molecular fingerprint tree, 82.5% approved and 63.0% clinical trial NPLDs are clustered in 60 drug-productive clusters (DCs) partly due to their preferential binding to 45 privileged target-site classes. The distribution patterns of the NPLDs are distinguished from those of the bioactive natural products. 11.7% of the NPLDs in these DCs have remote-similarity relationship with the nearest NPLD in their own DC. The majority of the new NPLDs emerge from preexisting DCs. The usefulness of the derived knowledge for NPLD discovery was demonstrated by the recognition of the new NPLDs of 2013-2014 approved drugs.
一些药物的天然产物先导物（NPLD）已被发现聚集在化学空间中。这种聚集现象的程度、详细模式和机制尚未得到充分研究，其在NPLD发现中的实用性需要更广泛地测试。在这项工作中，我们生成并评估了749种2013年前批准的小分子药物和263种临床试验小分子药物中的442种NPLD在由137,836种非冗余天然产物的分子支架和指纹树所代表的化学空间中的分布模式。在分子支架树中，62.7%的已批准NPLD和37.4%的临床试验NPLD聚集在62个具有药物生产能力的支架/支架分支中。在分子指纹树中，82.5%的已批准NPLD和63.0%的临床试验NPLD聚集在60个具有药物生产能力的簇（DC）中，部分原因是它们优先结合45种特权靶位点类别。NPLD的分布模式与生物活性天然产物的分布模式不同。这些DC中的11.7%的NPLD与其所在DC中最近的NPLD具有远程相似关系。大多数新的NPLD来自现有的DC。2013 - 2014年批准药物的新NPLD的识别证明了所获得的知识在NPLD发现中的实用性。
