ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-trained classifier model, ESM2-GRU, for ACP prediction to make it easier to predict ACPs, gain a better understanding of the structural and functional differences of anti-cancer peptides, and optimize the design for the development of more effective anti-cancer treatment strategies. The model is made up of the ESM2 pre-trained model, a bidirectional GRU recurrent neural network, and a fully connected layer. ACP sequences are first fed into the ESM2 model, which then expands the dimensions before feeding the findings back into the bidirectional GRU recurrent neural network. Finally, the fully connected layer generates the ultimate output. Experimental validation demonstrates that the ESM2-GRU model greatly improves classification performance on the benchmark dataset ACP606, with AUC, ACC, and MCC values of 0.975, 0.852, and 0.738, respectively. This exceptional prediction potential helps to identify specific types of anti-cancer peptides, improving their targeting and selectivity and, therefore, furthering the development of tailored medicine and treatments. [Display omitted] •The ACP predictor model ESM2-GRU consists of ESM2,a two-way GRU recurrent neural network and a fully connected layer.•ESM2-GRU helps understand ACP's structural and functional differences and develop anti-cancer therapeutic strategies.•ESM2-GRU improved the classification performance with AUC,ACC and MCC values of 0.975,0.852 and 0.738,respectively,on ACP606.•ESM2-GRU helps to identify specific anticancer peptides and facilitates the development of customized drugs and therapeutics.