Abstract
The identification of possible targets for a known compound by its sole molecular representation is one of the most important tasks for drug design and development. In this work, a methodology is proposed for target identification using supervised machine learning. To predict drug binding targets, classification models across targets were constructed using the k-NN algorithm by integrating multiple data types. Two different groups of descriptors are used: 1) Morgan’s fingerprint and 2) general molecular properties of interest. The findings demonstrate that the k-NN classification models achieved a higher f1-score with descriptors based on molecular properties of interest with 0.7 in comparison to the Morgan fingerprint descriptors that achieved a score of 0.57 or the fusion of both with a score of 0.58.
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Jimenes-Vargas, K., Perez-Castillo, Y., Tejera, E., Munteanu, C.R. (2023). Exploring Target Identification for Drug Design with K-Nearest Neighbors’ Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_20
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