Abstract
BackgroundComputational 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.ResultsTo 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.ConclusionsOur results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
Highlights
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
Using cancer cell drug sensitivity as a benchmark, our results clearly shows that Latent Target Interaction Profile (LTIP) outperforms molecular fingerprints in many cases
LTIP is more accurate and robust than molecular fingerprints in predicting chemical anti-cancer activities We have evaluated the performance of five of the stateof-the-art algorithms (RF, Random Forest Extra Tees Regressor (RF_EXTR), support vector machines (SVR), k-nearest neighbors regressor (KNR), and XGB) with all possible combinations between seven cancer cell lines and fingerprints representations of the chemical compounds
Summary
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. The chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. The genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. The chemical modulation of biological activity is a complex process [3] It starts from the interaction of chemicals with genome-wide macromolecular targets in the cell.
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