Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad; Shi, Jade; Gomes, Joseph; Nguyen, Quang Luu; Ho, Stephen; Sloane, Jack L.; Wender, Paul A.; Pande, Vijay S.

doi:10.1021/acscentsci.7b00303

Cited by 494 publications

(651 citation statements)

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“…Recent developments include fingerprint based representations of the molecular graph [204,205] or the 3D structure. [144,149,151,152] To date, machine learning based systems already outperform traditional methods in chemistry including examination by human experts and database search in several fields of work. [144,149,151,152] To date, machine learning based systems already outperform traditional methods in chemistry including examination by human experts and database search in several fields of work.…”

Section: Discussionmentioning

confidence: 99%

“…Some of the most widely used systems are ChemPlanner, [141] PathFinder, ICSynth, LHASA, CAMEO, SOPHIA, and EROS. [151] Copyright 2017, American Chemical Society. More recent approaches to the prediction of reactions and retrosynthesis may solve several problems that limit the systems used so far.…”

Section: Synthetic Accessibilitymentioning

confidence: 99%

“…Examples for such models have been presented for reaction prediction as well as retrosynthesis (for a schematic summary see Figure 5b). [150][151][152] These methods, consisting in most of the cases of seq2seq approaches (Figure 5c) that are well known for language translation, don't need any prior rule extraction. [149] Monte Carlo tree search with an expansion policy network guides in a second step the search for the most suitable chemical route.…”

Section: Synthetic Accessibilitymentioning

confidence: 99%

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Toward Design of Novel Materials for Organic Electronics

et al. 2019

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organic/inorganic perovskites solar cells [6] to printable electronic circuits based on organic field-effect transistors (OFETs). [7] While OLED displays outperform their inorganic counterparts in terms of energy efficiency, [8] scientific and technical challenges concerning the stability and processability of the organic materials used in large-area OLEDs and organic solar cells remain. New challenges arise from applications, such as displays on flexible substrates, OLED lightning, large area displays as well as for printable or solution processable larger area solar cells. [8] Many of the remaining challenges are material related, e.g., the low mobility of charge carriers in organic materials in general and in amorphous organic semiconductors in particular. There are other materials related issues, such as limited OLED life times due to unstable blue hosts and emitters, [9] low fill factors, and therefore reduced power conversion efficiencies of organic solar cells, [10,11] low conductivity and high costs of organic charge transport layers of perovskite solar cells [6,12,13] and low conductivity and hard processability of crystalline OFET materials. [14] Conductivity and injection can be improved by doping the organic thin films with molecular dopants with high electron affinities (p-type) [15] or low ionization energies (n-type). [16] The doping mechanism of organic materials is in many cases not well understood, [17] making material and device optimization a costly experimental endeavor.The development of better materials is presently based on chemical insight, in part guided by theoretical understanding, or the experimental screening of large numbers of compounds. Given the size of the potentially available chemical space this remains a costly and time-consuming approach. Recent successes in experimental design of novel materials and concepts include the development of a stable strong molecular n-type dopant, [16] a study about the quantitative relation between interaction parameter, miscibility, and function of conjugated polymer donors and small-molecule acceptors for bulk heterojunctions as used in organic solar cells [18] the development of a universal strategy for ohmic hole injection into organic semiconductors with high ionization energies [19] and many others. [20][21][22][23][24][25] Another example is the development of strategies to harvest triplet excitons in organic light emitting diodes. For this purpose, novel classes of emitter molecules were developed, which include thermally activated delayed fluorescence (TADF)-based molecules, [26][27][28][29][30][31][32] rotationally accessed spin-state inversion, [33] and radical-based emitters. [34] Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly inf...

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Section: Discussionmentioning

confidence: 99%

Section: Synthetic Accessibilitymentioning

confidence: 99%

Section: Synthetic Accessibilitymentioning

confidence: 99%

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Toward Design of Novel Materials for Organic Electronics

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“…Such grammars (of which there are many) are based on tree‐like traversal of molecules, and therefore reconstructing the original molecular topology requires persistent knowledge of the tree for which recurrent algorithms are essential. Notable examples of their use are found for the prediction of molecular properties ranging from atomization energies to biological activities as well as de novo drug design and retrosynthetic routes …”

Section: Topological Descriptorsmentioning

confidence: 99%

Representations and descriptors unifying the study of molecular and bulk systems

Rossi

Cumby

2019

Int J of Quantum Chemistry

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Establishing a unified framework for describing the structures of molecular and periodic systems is a long‐standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches—topological, atom‐density, and symmetry‐based—for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions.

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“…Recently, deep learning and reinforcement learning have been applied in retrosynthesis to increase the generalization as well as the prediction performance of rule-based methods. 9,[12][13][14] Liu et al 13 formulated retrosynthesis prediction as a translation task using a sequence-tosequence (seq2seq) architecture, where molecules are encoded as SMILES 15 sequences. The advantage of the seq2seq model is end-to-end and is able to access global information instead of only the reaction center.…”

Section: Introductionmentioning

confidence: 99%

Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation

Liu

Song

2019

Preprint

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Chemical retrosynthesis has been a crucial and challenging task in organic chemistry for several decades. In early years, retrosynthesis is accomplished by the disconnection approach which is labor-intensive and requires expert knowledge. Afterward, rulebased methods have dominated in retrosynthesis for years. In this study, we revisit the disconnection approach by leveraging deep learning (DL) to boost its performance and increase the explainability of DL. Concretely, we propose a novel graph-based deeplearning framework, named DeRetro, to predict the set of reactants for a target product by executing the process of disconnection and reactant generation orderly. Experimental results report that DeRetro achieves new state-of-the-art performance in predicting the reactants. In-depth analyses also demonstrate that even without the reaction type as input, DeRetro retains its retrosynthesis performance while other methods show a significant decrease, resulting in a large margin of 19% between DeRetro and previous state-of-the-art rule-based method. These results have established DeRetro as a power-ful and useful computational tool in solving the challenging problem of retrosynthetic analysis.

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Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

Cited by 494 publications

References 55 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

Representations and descriptors unifying the study of molecular and bulk systems

Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation

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