Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.
Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY, USA.
BMC Bioinformatics. 2019 Dec 20;20(Suppl 24):674. doi: 10.1186/s12859-019-3241-3.
Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery, and many other applications. Chemical fingerprints are a widely used feature to build machine learning models. However, the fingerprints that are derived from chemical structures ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. Nevertheless, the scope of direct application of the chemical-target interaction profile is limited due to the severe incompleteness, biasness, and noisiness of bioassay data.
To address the aforementioned problems, we developed a novel chemical representation method: Latent Target Interaction Profile (LTIP). LTIP embeds chemicals into a low dimensional continuous latent space that represents genome-scale chemical-target interactions. Subsequently LTIP can be used as a feature to build machine learning models. Using the drug sensitivity of cancer cell lines as a benchmark, we have shown that the LTIP robustly outperforms chemical fingerprints regardless of machine learning algorithms. Moreover, the LTIP is complementary with the chemical fingerprints. It is possible for us to combine LTIP with other fingerprints to further improve the performance of bioactivity prediction.
Our results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
在生物系统受到化学干扰时，对表型反应进行计算预测，在药物发现和许多其他应用中发挥着重要作用。化学指纹是构建机器学习模型的常用特征。然而，从化学结构中得出的指纹忽略了生物学背景，因此存在多个问题，例如活性悬崖和维度诅咒。从根本上说，生物活性的化学调节是一个多尺度的过程。是全基因组化学-靶标相互作用调节化学表型反应。因此，与化学结构相比，基因组规模的化学-靶标相互作用谱将更直接地与体外和体内活性相关。然而，由于生物测定数据的严重不完整性、偏差和噪声，化学-靶标相互作用谱的直接应用范围有限。
为了解决上述问题，我们开发了一种新的化学表示方法：潜在靶标相互作用谱（LTIP）。LTIP 将化学物质嵌入代表全基因组化学-靶标相互作用的低维连续潜在空间中。随后，LTIP 可以用作构建机器学习模型的特征。使用癌细胞系的药物敏感性作为基准，我们已经表明，无论机器学习算法如何，LTIP 都稳健地优于化学指纹。此外，LTIP 与化学指纹互补。将 LTIP 与其他指纹相结合，以进一步提高生物活性预测的性能，这是可能的。
我们的结果表明，LTIP 在特定情况下以及在一般的多尺度建模中，在生物活性的化学调节的预测建模中具有潜力。
