Peiretti Franck, Brunel Jean Michel
Faculté de Médecine, Aix Marseille Université, INSERM, INRA, C2VN, 27 Bd Jean Moulin, 13385 Marseille, France.
Faculté de Pharmacie, U1261, INSERM, UMR-MD1 (Membranes et Cibles Thérapeutiques), IRBA, Aix-Marseille Université, 27 Bd Jean Moulin, 13385 Marseille, France.
ACS Omega. 2018 Oct 16;3(10):13263-13266. doi: 10.1021/acsomega.8b01773. eCollection 2018 Oct 31.
On the basis of a recent article "" that appeared in , we had decided to highlight the way forward for artificial intelligence in chemistry. Synthesis of molecules remains one of the most important challenges in organic chemistry, and the standard approach involved by a chemist to solve a problem is based on experience and constitutes a repetitive, time-consuming task, often resulting in nonoptimized solutions. Thus, considering the recent phenomenal progresses that have been made in machine learning, there is little doubt that these systems, once fully operational in organic chemistry, will dramatically speed up development of new drugs and will constitute the future of chemistry.
基于最近发表在某刊物上的一篇文章,我们决定强调人工智能在化学领域的发展方向。分子合成仍然是有机化学中最重要的挑战之一,化学家解决问题的标准方法基于经验,是一项重复性、耗时的任务,常常导致非优化的解决方案。因此,鉴于机器学习最近取得的显著进展,毫无疑问,这些系统一旦在有机化学中全面投入使用,将极大地加速新药的研发,并将构成化学的未来。