pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.
pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.
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DOI：
10.1021/acs.jcim.1c00168
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发表时间：
2021-07-26
影响因子：
5.6
通讯作者：
Ascher DB
中科院分区：
文献类型：
--
作者：
Al-Jarf R;de Sá AGC;Pires DEV;Ascher DB
由于命中率有限，开发新的、有效的、安全的治疗癌症的药物仍然是一项具有挑战性和耗时的任务，限制了后续的开发努力。尽管定量结构-活性关系和基于机器学习的模型已经取得了令人印象深刻的进展，用于预测分子药效学和生物活性，但它们在识别针对多种细胞系具有抗癌特性的化合物方面取得了不同程度的成功。在这里，我们开发了一种新的预测工具，pdCSM-cancer，它使用小分子化学结构的基于图形的特征表示来准确预测可能对一种或多种癌细胞系有活性的分子。pdCSM-cancer是迄今为止开发的最全面的抗癌生物活性预测平台，包括基于生长抑制浓度（GI50%）效应实验数据的训练和验证模型，包括超过18,000种化合物，对9种肿瘤类型和74种不同的癌细胞系。在10倍交叉验证中，它的Pearson相关系数高达0.74，在独立的、无冗余的盲测试中，其可比性能高达0.67。利用这些细胞系特异性模型的见解，我们开发了一个通用的预测模型，以识别至少60个细胞系中活跃的分子。我们的最终模型在10倍交叉验证中实现了接收者工作特征曲线（AUC）下的面积高达0.94，在独立的非冗余盲测中高达0.94，优于其他方法。我们相信我们的预测工具将为优化和丰富筛选文库提供宝贵的资源，以鉴定有效和安全的抗癌分子。为了提供一个简单和集成的平台来快速筛选具有良好抗癌特性的潜在生物活性分子，我们将pdCSM-cancer免费在线提供。
The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure–activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson’s correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at .
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影响因子：
5.1
作者：
Kretschmer N;Rinner B;Deutsch AJ;Lohberger B;Knausz H;Kunert O;Blunder M;Boechzelt H;Schaider H;Bauer R
通讯作者：
Bauer R
影响因子：
5.1
作者：
Katariya, Kanubhai D.;Shah, Shailesh R.;Reddy, Dushyanth
通讯作者：
Reddy, Dushyanth
影响因子：
4.6
作者：
Kumar, Rahul;Chaudhary, Kumardeep;Raghava, Gajendra P. S.
通讯作者：
Raghava, Gajendra P. S.
DOI：
10.1093/bioinformatics/btv529
发表时间：
2016-01-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cortés-Ciriano I;van Westen GJ;Bouvier G;Nilges M;Overington JP;Bender A;Malliavin TE
通讯作者：
Malliavin TE
影响因子：
5.8
作者：
Hamad, Sabri;Adornetto, Gianluca;Campillos, Monica
通讯作者：
Campillos, Monica