The inhibition of Lysine-Specific Histone Demethylase 1 (LSD1) is a promising strategy for cancer treatment and targeting epigenetic mechanisms. This paper introduces a Probabilistic Conditional Generative Adversarial Network (Prob-cGAN), designed to predict the activity of LSD1 inhibitors. The Prob-cGAN was evaluated against state-of-the-art models using the ChEMBL database, demonstrating superior performance. Specifically, it achieved a top-1 $R^2$ of 0.739, significantly outperforming the Smiles-Transformer model at 0.591 and the baseline cGAN at 0.488. Furthermore, it recorded a lower $RMSE$ of 0.562, compared to 0.708 and 0.791 for the Smiles-Transformer and cGAN models respectively. These results highlight the potential of Prob-cGAN to enhance drug design and advance our understanding of complex biological systems through machine learning and bioinformatics.

翻译：赖氨酸特异性组蛋白去甲基化酶1（LSD1）的抑制是癌症治疗及靶向表观遗传机制的一种有前景的策略。本文提出一种概率条件生成对抗网络（Prob-cGAN），旨在预测LSD1抑制剂的活性。该模型基于ChEMBL数据库与前沿模型进行了对比评估，展现出优越性能。具体而言，其取得了0.739的top-1 $R^2$值，显著优于Smiles-Transformer模型的0.591和基准cGAN模型的0.488。此外，其记录的$RMSE$为0.562，低于Smiles-Transformer模型的0.708和cGAN模型的0.791。这些结果凸显了Prob-cGAN在通过机器学习与生物信息学优化药物设计、增进对复杂生物系统理解方面的潜力。