Two ChEMBL-34 subsets (lead-like and drug-like molecules)

Two subsets of molecules from ChEMBL-34[1]. Those molecular datasets might be useful to people training molecular generators. After decompression, you will get:chembl34_stable_ES_OA_LL.smi: 585,272 molecules.chembl34_stable_ES_OA_DL.smi: 756,420 molecules. stable=non-reactive molecules (filtered-out reactive functional groups from [5]).https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_stable.py ES=Easy Synthesis (SAscore <= 3.0) [2].https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_SA.py OA=Orally Available (according to a classifier trained on the dataset from [6]). LL=Lead-Like (almost the definition from [3]).https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_lead.py DL=Drug-Like (definition from [4]).https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_drug.py Bibliography Zdrazil, B., Felix, E., Hunter, F., Manners, E. J., Blackshaw, J., Corbett, S., ... & Leach, A. R. (2024). The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic acids research, 52(D1), D1180-D1192. https://doi.org/10.1093/nar/gkad1004 Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1, 1-11. https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8 Hann, M. M., & Oprea, T. I. (2004). Pursuing the leadlikeness concept in pharmaceutical research. Current opinion in chemical biology, 8(3), 255-263. https://doi.org/10.1016/j.cbpa.2004.04.003 Tran-Nguyen, V. K., Jacquemard, C., & Rognan, D. (2020). LIT-PCBA: an unbiased data set for machine learning and virtual screening. Journal of chemical information and modeling, 60(9), 4263-4273. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155 Lisurek, M., Rupp, B., Wichard, J., Neuenschwander, M., von Kries, J. P., Frank, R., ... & Kühne, R. (2010) Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept. Molecular diversity, 14, 401-408. https://link.springer.com/article/10.1007/s11030-009-9187-z Falcon-Cano, G., Molina, C., & Cabrera-Perez, M. A. (2020). ADME prediction with KNIME: development and validation of a publicly available workflow for the prediction of human oral bioavailability. Journal of chemical information and modeling, 60(6), 2660-2667. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019本数据集包含源自ChEMBL-34[1]的两组分子子集，可供训练分子生成模型的研究人员使用。 解压后将获得以下两个SMI格式分子文件： - chembl34_stable_ES_OA_LL.smi：包含585,272个分子 - chembl34_stable_ES_OA_DL.smi：包含756,420个分子 各文件后缀的含义如下： stable（稳定）：指无反应活性的分子，已通过文献[5]中的方法过滤掉反应性功能基团，处理脚本详见：https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_stable.py ES（易合成）：指合成可及性评分（Synthetic Accessibility Score, SAscore）≤3.0的分子[2]，处理脚本详见：https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_SA.py OA（口服生物可利用）：指基于文献[6]数据集训练的分类器所判定的具有口服生物利用性的分子 LL（类先导，Lead-Like）：指近似符合文献[3]中定义的类先导分子，处理脚本详见：https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_lead.py DL（类药，Drug-Like）：指符合文献[4]中定义的类药分子，处理脚本详见：https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_drug.py 参考文献： [1] Zdrazil B, Felix E, Hunter F, et al. 2023年版ChEMBL数据库：覆盖多生物活性数据类型与时间跨度的药物发现平台[J]. 核酸研究, 2024, 52(D1): D1180-D1192. https://doi.org/10.1093/nar/gkad1004 [2] Ertl P, Schuffenhauer A. 基于分子复杂度与片段贡献的类药分子合成可及性评分估算[J]. 化学信息学杂志, 2009, 1:1-11. https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8 [3] Hann MM, Oprea TI. 药物研发中的类先导性概念探究[J]. 当代化学生物学观点, 2004, 8(3):255-263. https://doi.org/10.1016/j.cbpa.2004.04.003 [4] Tran-Nguyen VK, Jacquemard C, Rognan D. LIT-PCBA：用于机器学习与虚拟筛选的无偏数据集[J]. 化学信息与建模杂志, 2020, 60(9):4263-4273. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155 [5] Lisurek M, Rupp B, Wichard J, et al. 基于最大公共子结构概念设计含潜在生物活性分子的化学文库[J]. 分子多样性, 2010,14:401-408. https://link.springer.com/article/10.1007/s11030-009-9187-z [6] Falcon-Cano G, Molina C, Cabrera-Perez MA. 基于KNIME的ADME（吸收-分布-代谢-排泄）预测：人类口服生物利用度预测公开工作流的开发与验证[J]. 化学信息与建模杂志, 2020,60(6):2660-2667. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019