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Hence recasting the DTI prediction task as a regression problem may be one way to solve this problem. This paper proposes a novel convolutional neural network with an attention-based bidirectional long short-term memory (CNN-AttBiLSTM), a new deep-learning hybrid model for predicting drug-target binding affinities. Secondly, it can be arduous and time-intensive to tune the hyperparameters of a CNN-AttBiLSTM hybrid model to augment its performance. To tackle this issue, we suggested a Memetic Particle Swarm Optimization (MPSOA) algorithm, for ascertaining the best settings for the proposed model. According to experimental results, the suggested MPSOA-based CNN- Att-BiLSTM model outperforms baseline techniques with a 0.90 concordance index and 0.228 mean square error in DAVIS dataset, and 0.97 concordance index and 0.010 mean square error in the KIBA dataset.<\/jats:p>","DOI":"10.3233\/idt-230145","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T12:01:33Z","timestamp":1689681693000},"page":"1455-1474","source":"Crossref","is-referenced-by-count":0,"title":["Bio-inspired algorithm-based hyperparameter tuning for drug-target binding affinity prediction in healthcare"],"prefix":"10.1177","volume":"17","author":[{"given":"Moolchand","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Suman","family":"Deswal","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-230145_ref1","doi-asserted-by":"crossref","first-page":"115525","DOI":"10.1016\/j.eswa.2021.115525","article-title":"EA-based 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