Machine intelligence for chemical reaction space

Abstract

Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances and more sustainable processes. The current wave of machine intelligence is revolutionizing all data-rich disciplines. Machine intelligence has emerged as a potential game-changer for chemical reaction space exploration and the synthesis of novel molecules and materials. Herein, we will address the recent development of data-driven technologies for chemical reaction tasks, including forward reaction prediction, retrosynthesis, reaction optimization, catalysts design, inference of experimental procedures, and reaction classification. Accurate predictions of chemical reactivity are changing the R&D processes and, at the same time, promoting an accelerated discovery scheme both in academia and across chemical and pharmaceutical industries. This work will help to clarify the key contributions in the fields and the open challenges that remain to be addressed.

Low-cost prediction of molecular and transition state partition functions via machine learning

“…45,46 In the future, we plan to generate datasets for systems with more atoms, and to evaluate anharmonic partition functions to train new estimators. Transfer 47 or delta 48 learning might be used to accelerate these tasks.…”

Open data and algorithms for open science in AI-driven molecular informatics

“…Recently, Strieth-Kalthoff et al demonstrated the benefit that emerges from the usage of real experimental data for machine learning-based chemical yield predictions [12] while the prediction of reaction outcomes and yields remains a challenge [49]. Nonetheless, there have been impressive developments using attention-based deep learning methods to explore the chemical reaction space [50]. Schwaller et al have shown that the attention matrix weights of transformers that have been trained on unlabelled chemical reaction data can be used to determine accurate atom mappings [51].…”

“…Again, the described advances are exemplary cases of the synergy of deep learning-based models and the availability of training data. There are datasets extracted from US patents [52,[56][57][58][59], the scientific literature [60] and high-throughput experiments (HTE) [61] available [50]. Recently, the Open Reaction Database (ORD) has been launched as a platform to replace unstructured reaction data in the supporting information of publications [62].…”

PaRoutes: towards a framework for benchmarking retrosynthesis route predictions

“…Such methods date back to the 60's and the early work of Corey, 3 although the research has intensified in the last decade due to the increased interest in machine learning and artificial intelligence. 4 At the heart of retrosynthesis analysis is a method that is capable of predicting disconnections on a compound and thereby producing precursors. Such methods are typically referred to as one-step retrosynthesis or single-step retrosynthesis.…”