RESUMEN
Sustainability is here to stay. As businesses migrate away from fossil fuels and toward renewable sources, chemistry will play a crucial role in bringing the economy to a point of net-zero emissions. In fact, chemistry has always been at the forefront of developing new or enhanced materials to fulfill societal demands, resulting in goods with appropriate physical or chemical qualities. Today, the main focus is on developing goods and materials that have a less negative impact on the environment, which may include (but is not limited to) leaving behind smaller carbon footprints. Integrating data and AI can speed up the discovery of new eco-friendly materials, predict environmental impact factors for early assessment of new technological integration, enhance plant design and management, and optimize processes to reduce costs and improve efficiency, all of which contribute to a more rapid transition to a sustainable system. In this perspective, we hint at how AI technologies have been employed so far first, at estimating sustainability metrics and second, at designing more sustainable chemical processes.
RESUMEN
The RXN for Chemistry project, initiated by IBM Research Europe - Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 2022.
RESUMEN
Over the past four years, several research groups demonstrated the combination of domain-specific language representation with recent NLP architectures to accelerate innovation in a wide range of scientific fields. Chemistry is a great example. Among the various chemical challenges addressed with language models, retrosynthesis demonstrates some of the most distinctive successes and limitations. Single-step retrosynthesis, the task of identifying reactions able to decompose a complex molecule into simpler structures, can be cast as a translation problem, in which a text-based representation of the target molecule is converted into a sequence of possible precursors. A common issue is a lack of diversity in the proposed disconnection strategies. The suggested precursors typically fall in the same reaction family, which limits the exploration of the chemical space. We present a retrosynthesis Transformer model that increases the diversity of the predictions by prepending a classification token to the language representation of the target molecule. At inference, the use of these prompt tokens allows us to steer the model towards different kinds of disconnection strategies. We show that the diversity of the predictions improves consistently, which enables recursive synthesis tools to circumvent dead ends and consequently, suggests synthesis pathways for more complex molecules.
RESUMEN
Data-driven approaches to retrosynthesis are limited in user interaction, diversity of their predictions, and recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt-based inference in natural language processing to the task of chemical language modeling. We show that by using a prompt describing the disconnection site in a molecule we can steer the model to propose a broader set of precursors, thereby overcoming training data biases in retrosynthetic recommendations and achieving a 39% performance improvement over the baseline. For the first time, the use of a disconnection prompt empowers chemists by giving them greater control over the disconnection predictions, which results in more diverse and creative recommendations. In addition, in place of a human-in-the-loop strategy, we propose a two-stage schema consisting of automatic identification of disconnection sites, followed by prediction of reactant sets, thereby achieving a considerable improvement in class diversity compared with the baseline. The approach is effective in mitigating prediction biases derived from training data. This provides a wider variety of usable building blocks and improves the end user's digital experience. We demonstrate its application to different chemistry domains, from traditional to enzymatic reactions, in which substrate specificity is critical.
RESUMEN
Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand.
RESUMEN
The world is on the verge of a new industrial revolution, and language models are poised to play a pivotal role in this transformative era. Their ability to offer intelligent insights and forecasts has made them a valuable asset for businesses seeking a competitive advantage. The chemical industry, in particular, can benefit significantly from harnessing their power. Since 2016 already, language models have been applied to tasks such as predicting reaction outcomes or retrosynthetic routes. While such models have demonstrated impressive abilities, the lack of publicly available data sets with universal coverage is often the limiting factor for achieving even higher accuracies. This makes it imperative for organizations to incorporate proprietary data sets into their model training processes to improve their performance. So far, however, these data sets frequently remain untapped as there are no established criteria for model customization. In this work, we report a successful methodology for retraining language models on reaction outcome prediction and single-step retrosynthesis tasks, using proprietary, nonpublic data sets. We report a considerable boost in accuracy by combining patent and proprietary data in a multidomain learning formulation. This exercise, inspired by a real-world use case, enables us to formulate guidelines that can be adopted in different corporate settings to customize chemical language models easily.