Rana Abhilash, Chauhan Ruchi, Mottafegh Amirreza, Kim Dong Pyo, Singh Ajay K
Department of Organic Synthesis and Process Chemistry, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
Commun Chem. 2024 Nov 1;7(1):251. doi: 10.1038/s42004-024-01330-z.
The reproducibility of chemical reactions, when obtaining protocols from literature or databases, is highly challenging for academicians, industry professionals and even now for the machine learning process. To synthesize the organic molecule under the photochemical condition, several years for the reaction optimization, highly skilled manpower, long reaction time etc. are needed, resulting in non-affordability and slow down the research and development. Herein, we have introduced the DigiChemTree backed with the artificial intelligence to auto-optimize the photochemical reaction parameter and synthesizing the on demand library of the molecules in fast manner. Newly, auto-generated digital code was further tested for the late stage functionalization of the various active pharmaceutical ingredient.
从文献或数据库获取实验方案时,化学反应的可重复性对院士、行业专业人士而言极具挑战性,即便对机器学习过程亦是如此。要在光化学条件下合成有机分子,需要数年进行反应优化、大量高技能人力以及较长的反应时间等,这导致成本过高且减缓了研发进程。在此,我们引入了基于人工智能的DigiChemTree,以自动优化光化学反应参数并快速合成按需定制的分子库。最近,新生成的数字代码还针对各种活性药物成分的后期功能化进行了测试。