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Synthetic organic chemistry driven by artificial intelligence


Synthetic organic chemistry underpins several areas of chemistry, including drug discovery, chemical biology, materials science and engineering. However, the execution of complex chemical syntheses in itself requires expert knowledge, usually acquired over many years of study and hands-on laboratory practice. The development of technologies with potential to streamline and automate chemical synthesis is a half-century-old endeavour yet to be fulfilled. Renewed interest in artificial intelligence (AI), driven by improved computing power, data availability and algorithms, is overturning the limited success previously obtained. In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature. By examining the underlying concepts, we aim to demystify AI for bench chemists in order that they may embrace it as a tool rather than fear it as a competitor, spur future research by pinpointing the gaps in knowledge and delineate how chemical AI will run in the era of digital chemistry.

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Fig. 1: Variability in chemical reaction data available from patents (1976–2016).
Fig. 2: Similarity search for in silico retrosynthesis analysis.
Fig. 3: Artificial intelligence tools for retrosynthetic analysis.
Fig. 4: Comparison of two methods for the prediction of reaction products.
Fig. 5: Active learning for the optimization of reaction conditions.
Fig. 6: Automated discovery of new chemistry.
Fig. 7: Networking robots.


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A.F.A. acknowledges Fundação para a Ciência e Tecnologia (FCT) Portugal for financial support through a PhD grant (PD/BD/143125/2019). T.R. is an investigador auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges FCT/FEDER (02/SAICT/2017, grant 28333) for funding. The authors thank the reviewers for their comments.

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Correspondence to Tiago Rodrigues.

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Natural-language processing

Area of computer science that deals with the recognition, processing and analysis of human (natural) language.


(SMILES arbitrary target specification). A notation for the accurate substructural feature identification and atom typing.


(Simplified molecular-input line-entry system). A notation to describe chemical structure using ASCII strings.

Morgan fingerprints

A method to map substructural information into a bit string. The bit length (size) and detail of encoded features are defined by the user.

Tanimoto index

A method to quantify similarity (ranging from 0 to 1) between molecules. Complete dissimilarity equates to 0 and full identity equals 1.

Softmax layer

A method that normalizes a vector of length j into a probability distribution containing J probabilities in the interval [0,1]. The sum of all probabilities equals 1.0.

Gaussian processes

A machine-learning method giving a probability distribution over a number of possible functions. A prior belief regarding an event is refined through Bayesian inference as data builds up.

Linear discriminant analysis

(LDA). A machine-learning method that finds linear combinations of features that separate classes, prior to dimensionality reduction and classification.

Support-vector machine

(SVM). A machine-learning method that separates data points in hyperspace through mathematical functions called kernels.

Transfer learning

A method for fine-tuning a model trained on a larger set of related data. The method is employed when limited data are available to answer a research question.

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de Almeida, A.F., Moreira, R. & Rodrigues, T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 3, 589–604 (2019).

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