Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

Klucznik, Tomasz; Mikulak-Klucznik, Barbara; McCormack, Michael P.; Lima, Heather M.; Szymkuć, Sara; Bhowmick, Manishabrata; Molga, Karol; Zhou, Yubai; Rickershauser, Lindsey; Gajewska, Ewa; Toutchkine, Alexei; Dittwald, Piotr; Startek, Michał; Kirkovits, Gregory J.; Roszak, Rafał; Adamski, Ariel; Sieredzińska, Bianka; Mrksich, Milan; Trice, Sarah L. J.; Grzybowski, Bartosz A.

doi:10.1016/j.chempr.2018.02.002

Cited by 280 publications

(319 citation statements)

References 30 publications

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“…[58][59][60] Due to the complexity of chemical reactions, molecules designed computationally might be only available with high synthetic effort and high investments in resources and time. Therefore, many scientists worldwide in academia [62] and industry [63,64] are working on solutions to provide synthetic information automatically without prior consultation of chemistry experts. There are two general procedures that enable scientists to evaluate the accessibility of chemical compounds: the forward prediction of a reaction outcome as well as the analysis of possible reaction pathways yielding the desired compound (retrosynthesis, see Figure 1d).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…Synthia has been validated experimentally as an efficient toolkit for complex products. 16 Synthia relies on human knowledge of organic synthesis and the encoding of organic rules, 17 which took their team for more than 15 years. It will not be practical to manually collect all the knowledge of organic synthesis considering the exponential growth of the number of published reactions.…”

Section: Introductionmentioning

confidence: 99%

Automatic Retrosynthetic Pathway Planning Using Template-free Models

Lin¹,

Xu²,

Pei³

et al. 2019

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We present an attention-based Transformer model for automatic retrosynthesis route planning. Our approach starts from reactants prediction of single-step organic reactions for given products, followed by Monte Carlo tree search-based automatic retrosynthetic pathway prediction.Trained on two datasets from the United States patent literature, our models achieved a top-1 prediction accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of SMILES, respectively, which is the best up to now to our knowledge. We also demonstrate the application potential of our model by successfully performing multi-step retrosynthetic route planning for four case products, i.e., antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of human acute-myeloid-leukemia cells and a complex intermediate of drug candidate. Further, by using heuristics Monte Carlo tree search, we achieved automatic retrosynthetic pathway searching and successfully reproduced published synthesis pathways. In summary, our model has achieved the stateof-the-art performance on single-step retrosynthetic prediction and provides a novel strategy for automatic retrosynthetic pathway planning.

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“…In most cases, ICHO+ outperformed the other methods assigning better ranks (see the colored bars and pathway‐averaged ranks on the right) to synthetic choices found in the actual pathways. For instance, in the synthesis of the BRD 7/9 inhibitor (Figure a), the key first step (three‐component aza‐Henry reaction with zero reactions of the same type in the training set) was ranked #1 by ICHO+ but only #26 by SW2+, which, as discussed, cannot learn about reactions it has never seen. SMALLER did well on this step (because the reaction offered high structural simplification) but lost out on steps in which only some functional groups were manipulated (reactions with ranks #21, #41).…”

Section: Figurementioning

confidence: 99%

“…Average rankings for entire pathways (⟨Rank⟩) are given on the right of each panel. Syntheses of a) BRD 7/9 inhibitor, b) (+)‐synosutine, c) seimatopolide A, and d) bimatoprost . For clarity, and to mimic Chematica's implicit treatment of protections (ref.…”

Section: Figurementioning

confidence: 99%

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Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning

et al. 2019

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When computers plan multistep syntheses, they can rely either on expert knowledge or information machine‐extracted from large reaction repositories. Both approaches suffer from imperfect functions evaluating reaction choices: expert functions are heuristics based on chemical intuition, whereas machine learning (ML) relies on neural networks (NNs) that can make meaningful predictions only about popular reaction types. This paper shows that expert and ML approaches can be synergistic—specifically, when NNs are trained on literature data matched onto high‐quality, expert‐coded reaction rules, they achieve higher synthetic accuracy than either of the methods alone and, importantly, can also handle rare/specialized reaction types.

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Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

Cited by 280 publications

References 30 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

Automatic Retrosynthetic Pathway Planning Using Template-free Models

Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning

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