Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.

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深度学习促进对具有潜在微管抑制活性的天然产物的筛选

天然微管抑制剂，如紫杉醇和伊沙匹隆，是新型药物的关键来源，对抗肿瘤化疗有相当大的影响。鼓励天然产物化学家创造新的方法，从庞大的天然产物库中筛选新一代微管抑制剂。最近，人工智能在药物发现中的应用取得了重大进展。尤其是深度学习算法，在从庞大的化合物库中快速筛选有效线索和生产具有理想特征的新化合物方面显示出了希望。我们使用深度神经网络在天然产品中寻找有效的 β-微管抑制剂。刺五加素、bruceine D (BD)、佛波醇 12-肉豆蔻酸酯 13-乙酸酯 (PMA) 是三种高效的天然化合物，已被发现可作为 β-微管抑制剂。总之，本文描述了使用深度学习来筛选有效的 β-微管抑制剂。这项研究还证明了利用深度学习从天然产物中开发用于治疗更广泛疾病的药物的可能性。