Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Mini Rev Med Chem. 2021;21(18):2788-2800. doi: 10.2174/1389557521666210401091147.
In silico ADMET models have progressed significantly over the past ~4 decades, but still, the pharmaceutical industry is vexed by the late-stage toxicity failure of lead molecules. This problem of late-stage attrition of the drug candidates because of adverse ADMET profile motivated us to analyze the current role and status of different in silico tools along with the rise of machine learning (ML) based program for ADMET prediction. In this review, we have differentiated AI from traditional in silico tools because, unlike traditional in silico tools where the final decision is made manually, AI automates the decision-making prerogative of humans. Due to the large volume of literature in this field, we have considered the publications in the last two years for our review. Overall, from the literature reviewed, deep neural networks (DNN) algorithm or deep learning seems to be the future of ML-based prediction models. DNNs have shown the ability to learn from more complex data and this gives DNN an edge over other ML algorithms to be applied for ADMET prediction. Our result also suggests that we need closer collaboration between the ADMET data generators and those who are employing ML-based tools on this generated data to build predictive models, so that more accurate models could be developed. Overall, our study concludes that ML is still a work in progress and its appetite for data has not been sated yet. It needs loads of more quality data and still some time to prove its real worth in predicting ADMET.
在过去的大约 40 年中，计算机辅助药物设计（ADMET）模型取得了显著进展，但制药行业仍深受先导化合物后期毒性失败的困扰。由于不良的 ADMET 特征，候选药物在后期淘汰的问题促使我们分析了不同计算机辅助工具的当前作用和地位，以及基于机器学习（ML）的 ADMET 预测程序的兴起。在这篇综述中，我们将人工智能与传统计算机辅助工具区分开来，因为与传统计算机辅助工具不同，传统计算机辅助工具的最终决策是手动做出的，而人工智能则实现了人类决策的自动化。由于该领域的文献量很大，我们仅考虑了过去两年的出版物进行综述。总的来说，从综述的文献来看，深度神经网络（DNN）算法或深度学习似乎是基于 ML 的预测模型的未来。DNN 已经显示出从更复杂的数据中学习的能力，这使得 DNN 比其他 ML 算法在应用于 ADMET 预测方面具有优势。我们的结果还表明，我们需要在 ADMET 数据生成者和那些在生成的数据上使用基于 ML 的工具的人员之间进行更紧密的合作，以构建预测模型，从而开发出更准确的模型。总的来说，我们的研究得出结论，ML 仍然是一个正在进行的工作，它对数据的需求尚未得到满足。它需要大量更多的高质量数据，并且仍需要一些时间来证明其在 ADMET 预测中的真正价值。
