A releasable disulfide carbonate linker for polyethyleneimine (PEI)-based gene vectors

Feng, Liandong; Xie, Aming; Liu, Yangyang; Zhang, Jianfa; Li, Shufeng; Dong, Wei

doi:10.1039/c4nj00699b

Cited by 21 publications

(15 citation statements)

References 37 publications

(42 reference statements)

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“…Some of the most widely used systems are ChemPlanner, [141] PathFinder, ICSynth, LHASA, CAMEO, SOPHIA, and EROS. [142] Rule-based chemical expert systems can be very helpful for specific chemical questions but suffer from disadvantages that limit their use in particular for virtual materials design: 1) many predictions or proposed reaction pathways are not precise enough or at least not match the decisions human experts would make, 2) due to performance reasons, the expert systems are not designed to handle high throughput prediction, 3) the underlying rules of the expert systems have to be encoded at least in parts manually which means that any logic that was missed by human beings is lacking in the information system, and 4) most of the reliable systems are only commercially available and very expensive. 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%

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: Synthetic Accessibilitymentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…It has repeatedly been stated that "de novo design methods cannot identify ap erfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist." [82] This statement is certainly true for the earlier or more traditional molecule construction programs,w hich have repeatedly resulted in overly complex chemical structures.B ased on the evidence provided by the practical application of reaction-driven de novo design, we feel that some of the contemporary design tools are in fact able to generate readily synthesizable chemical matter with the desired properties.Keeping in mind the imperfections of automated chemical synthesis planning and reaction pathway design, [83] the combination of AI-driven generative molecular design models with advanced forward-and retrosynthesis algorithms offers ample opportunity for future molecular discovery.…”

Section: Are We Nearly There Yet?mentioning

confidence: 99%

Automated De Novo Drug Design: Are We Nearly There Yet?

2019

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Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

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“…4 Computer-aided synthesis planning has been well-reviewed over the past years. [5][6][7][8][9][10][11] According to a recent review, 11 computer-aided retrosynthetic route planning strategies can be clustered into two main categories: template-based and template-free methods. Template-based methods, including the LHASA soware, 4,12 have been applied since the philosophy of retrosynthetic analysis was put forward by E. J. Corey.…”

Section: Introductionmentioning

confidence: 99%

Automatic retrosynthetic route planning using template-free models

et al. 2020

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Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Here we present a template-free approach that is independent of reaction templates, rules, or atom mapping, to implement automatic retrosynthetic route planning. We treated each reaction prediction task as a data-driven sequence-to-sequence problem using the multi-head attention-based Transformer architecture, which has demonstrated power in machine translation tasks. Using reactions from the United States patent literature, our end-to-end models naturally incorporate the global chemical environments of molecules and achieve remarkable performance in top-1 predictive accuracy (63.0%, with the reaction class provided) and top-1 molecular validity (99.6%) in one-step retrosynthetic tasks. Inspired by the success rate of the one-step reaction prediction, we further carried out iterative, multi-step retrosynthetic route planning for four case products, which was successful. We then constructed an automatic data-driven end-to-end retrosynthetic route planning system (AutoSynRoute) using Monte Carlo tree search with a heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The end-to-end model for reaction task prediction can be easily extended to larger or customer-requested reaction databases. Our study presents an important step in realizing automatic retrosynthetic route planning.

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A releasable disulfide carbonate linker for polyethyleneimine (PEI)-based gene vectors

Cited by 21 publications

References 37 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

Automated De Novo Drug Design: Are We Nearly There Yet?

Automatic retrosynthetic route planning using template-free models

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