Target identification | FGP

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The understanding of a chemical component’s mechanism of action and side effects, as well as drug discovery and systems biology, depend on the prediction of compound-target interaction. So, this web tool publishes some newly developed target-centric models employing different types of molecular descriptions and machine learning algorithms. Additionally, a consensus strategy based on these models is also published as a potential advancement above individual forecasts.

e.g. CC(=O)O[C@H]1CC[C@@]2(C)[C@@H](CC[C@]3(C)[C@@H]2CC=C4[C@@H]5CC(C)(C)CC[C@@]5(CC[C@@]34C)C(=O)OCc6ccccc6)[C@]1(C)C=O

For technical information about the algorithm, you can refer to:

Jimenes-Vargas, K., Pazos, A., Munteanu, C.R. et al. Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy. J Cheminform 16, 27 (2024).