by , , , , , ,
Abstract:
Discovering new reactions, optimizing their performance, and extending the syn-thetically accessible chemical space are critical drivers for major technologicaladvances and more sustainable processes. The current wave of machine intelligenceis revolutionizing all data-rich disciplines. Machine intelligence has emerged as apotential game-changer for chemical reaction space exploration and the synthesisof novel molecules and materials. Herein, we will address the recent developmentof data-driven technologies for chemical reaction tasks, including forward reactionprediction, retrosynthesis, reaction optimization, catalysts design, inference ofexperimental procedures, and reaction classification. Accurate predictions of chemi-cal reactivity are changing the R&D processes and, at the same time, promoting anaccelerated discovery scheme both in academia and across chemical and pharma-ceutical industries. This work will help to clarify the key contributions in the fieldsand the open challenges that remain to be addressed.
Reference:
Machine intelligence for chemical reaction space P. Schwaller, A. C. Vaucher, R. Laplaza, C. Bunne, A. Krause, C. Corminboeuf, T. LainoIn WIREs Computational Molecular Science Advanced Reviews, 2022
Bibtex Entry:
@article{schwaller22machine,
	Author = {Philippe Schwaller and Alain C. Vaucher and Ruben Laplaza and Charlotte Bunne and Andreas Krause and Clemence Corminboeuf and Teodoro Laino},
	Doi = {10.1002/wcms.1604},
	Journal = {WIREs Computational Molecular Science Advanced Reviews},
	Month = {March},
	Title = {Machine intelligence for chemical reaction space},
	Year = {2022}}