AndroPred: an artificial intelligence-based model for predicting androgen receptor inhibitors

Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. Understanding AR molecular mechanisms has led to the development of newer drugs that inhibit androgen production enzymes or block ARs. The FDA has approved a small number of AR-inhibiting drugs for use in PCa thus far, as the identification of novel AR inhibitors is difficult, expensive, time-consuming, and labor-intensive. To accelerate the process, artificial intelligence (AI) algorithms were employed to predict AR inhibitors using a dataset of 2242 compounds. Four machine learning (ML) and deep learning (DL) algorithms were used to train different prediction models based on molecular descriptors (1D, 2D, and molecular fingerprints). The DL-based prediction model outperformed the other trained models with accuracies of 92.18% and 93.05% on the training and test datasets, respectively. Our findings highlight the potential of DL, particularly the DNN model, as an effective approach for predicting AR inhibitors, which could significantly streamline the process of identifying novel AR inhibitors in PCa drug discovery. Further validation of these models using experimental assays and prospective testing of newly designed compounds would be valuable to confirm their predictive power and applicability in practical drug discovery settings. Communicated by Ramaswamy H. Sarma

雄激素受体（Androgen receptor, AR）作为一类类固醇受体，在前列腺癌（Prostate cancer, PCa）的发病机制中发挥关键作用。AR可调控参与细胞抗凋亡与增殖过程的基因转录，进而促进前列腺癌的发生发展。对AR分子机制的解析推动了新型靶向药物的研发，此类药物可抑制雄激素合成酶或阻断AR。截至目前，美国食品药品监督管理局（Food and Drug Administration, FDA）仅批准了少量可用于前列腺癌治疗的AR抑制剂类药物，这是因为新型AR抑制剂的筛选难度大、成本高昂、耗时漫长且劳动强度较高。为加速这一研发进程，研究团队借助人工智能（Artificial Intelligence, AI）算法，基于包含2242种化合物的数据集开展AR抑制剂的预测研究。本研究基于分子描述符（1维、2维及分子指纹），采用四种机器学习（Machine Learning, ML）和深度学习（Deep Learning, DL）算法训练了多款不同的预测模型。其中，基于深度学习的预测模型表现最优，其在训练集与测试集上的准确率分别达到92.18%与93.05%。本研究结果证实了深度学习（尤其是深度神经网络（Deep Neural Network, DNN）模型）在AR抑制剂预测中的应用潜力，该方法可显著简化前列腺癌药物研发中新型AR抑制剂的筛选流程。后续可通过实验检测对这些模型进行进一步验证，并对新设计的化合物开展前瞻性测试，以确认其预测能力与在实际药物研发场景中的适用性。本文由Ramaswamy H. Sarma提交。