|Title||Biological representation of chemicals using latent target interaction profile.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Ayed M, Lim H, Xie L|
|Date Published||2019 Dec 20|
|Keywords||Algorithms, Drug Discovery, Machine Learning|
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: Our results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
|Alternate Journal||BMC Bioinformatics|
|PubMed Central ID||PMC6924142|
|Grant List||R01 AG057555 / AG / NIA NIH HHS / United States |
R01 GM122845 / GM / NIGMS NIH HHS / United States
R01 LM011986 / LM / NLM NIH HHS / United States