探索抗癌药物的深度生成模型:前进之路

Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia
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引用次数: 0

摘要

药物发现是一个成本高、耗时长的过程,特别是由于临床试验失败的比例很高,因此花费巨大。因此,需要有新的范式来优化从新药鉴定到市场批准的各个阶段。人工智能(AI)技术使用的激增和深度学习(DL)的出现,为重新思考和重新设计药物发现的传统流程(包括全新分子设计)带来了巨大希望。在这方面,生成模型极大地影响了具有所需特性的分子的从头设计,并越来越多地融入到现实世界的药物发现活动中。在此,我们将简要评估近期在抗癌药物发现领域利用生成模型生成化学结构的案例研究。最后,我们将分析当前面临的挑战和局限性,以及克服这些挑战和局限性的可能策略,并概述推进这一令人兴奋的领域的潜在未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep generative models in the quest for anticancer drugs: ways forward
Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.
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