Abstract：
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model\'s commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.
摘要：
乳腺癌,一种高度强大和多样化的恶性肿瘤，主要影响全球女性，由于其复杂的遗传变异性，构成了重大威胁，使得准确诊断具有挑战性。各种疗法，如免疫疗法，放射治疗,根据癌症亚型和转移严重程度，广泛使用多种化疗方法，如药物再利用和联合治疗。我们的研究围绕针对RTK信号特异性潜在候选药物的创新药物发现策略，癌症中一个突出的靶向受体类别。要做到这一点，我们已经开发了一种基于多模态深度神经网络（MM-DNN）的QSAR模型，该模型集成了组学数据集以阐明基因组，蛋白质组表达数据，和药物反应，严格验证。结果显示R2值为0.917，RMSE值为0.312，证实了该模型值得称道的预测能力。RTK信号特异性药物分子的结构类似物来自PubChem数据库，其次是细致的筛选，以消除不同的化合物。利用基于MM-DNN的QSAR模型，我们预测了这些分子的生物活性，随后将它们分成三个不同的组。进行特征重要性分析。因此,我们成功确定了针对RTK信号通路中每个潜在下游调节蛋白定制的主要候选药物.这种方法通过去除非活性化合物，使药物开发的早期阶段更快，为抗击乳腺癌提供了一条充满希望的道路。
