Antibody-drug conjugate (ADC) has revolutionized the field of cancer
treatment in the era of precision medicine due to their ability to precisely
target cancer cells and release highly effective drug. Nevertheless, the
realization of rational design of ADC is very difficult because the
relationship between their structures and activities is difficult to
understand. In the present study, we introduce a unified deep learning
framework called ADCNet to help design potential ADCs. The ADCNet highly
integrates the protein representation learning language model ESM-2 and
small-molecule representation learning language model FG-BERT models to achieve
activity prediction through learning meaningful features from antigen and
antibody protein sequences of ADC, SMILES strings of linker and payload, and
drug-antibody ratio (DAR) value. Based on a carefully designed and manually
tailored ADC data set, extensive evaluation results reveal that ADCNet performs
best on the test set compared to baseline machine learning models across all
evaluation metrics. For example, it achieves an average prediction accuracy of
87.12%, a balanced accuracy of 0.8689, and an area under receiver operating
characteristic curve of 0.9293 on the test set. In addition, cross-validation,
ablation experiments, and external independent testing results further prove
the stability, advancement, and robustness of the ADCNet architecture. For the
convenience of the community, we develop the first online platform
(this https URL) for the prediction of ADCs activity based on the
optimal ADCNet model, and the source code is publicly available at
this https URL

基于深度学习的 ADCNet 模型通过学习 ADC 的抗原和抗体蛋白序列、连接物和有效药物的 SMILES 串以及药物 - 抗体比值等有意义的特征，成功实现了 ADCs 的活性预测，并在评估指标上优于基准机器学习模型，证明了其稳定性、先进性和鲁棒性。