Modelling Chemical Reasoning to Predict and Invent Reactions

Segler, Marwin; Waller, Mark P.

doi:10.1002/chem.201604556

Cited by 181 publications

(173 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%

“…In the past, automatic retrosynthesis or reaction prediction (see Figure 5a) required information from databases and/or the manual encoding of chemical rules. Newer developments include the automated extraction of reaction rules, [143] new models for chemical reasoning, [144] heuristics aided methods, [145] and the use of machine learning. Some of the most widely used systems are ChemPlanner, [141] PathFinder, ICSynth, LHASA, CAMEO, SOPHIA, and EROS.…”

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%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…However,t he rule-based approachh as severald rawbacks:F irst, rule-based expert systems cannot predict anythingo utside of their knowledge, whichr enders them unablet od iscover novel chemistry. [4] Second, the rules have to be compiled and curated.T hey can eitherb el aboriously encoded by human experts, or extracted algorithmically from data. This procedure can be difficult because mechanistic understanding is usually needed to encode whichn eighbouring functional groups influence the outcome of ar eaction.…”

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confidence: 99%

Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Segler

Waller

2017

Chemistry A European J

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470

472

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Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.

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“…Examples include Bioclipse [27, 28] and AMBIT [29–31]. The CDK has also played a role in a number of chemical studies, such as finding the maximally bridging rings in chemical structures [32], prediction of organic reactions [33], and bioactivities of compounds [34]. …”

Section: Introductionmentioning

confidence: 99%

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

Mayfield²,

et al. 2017

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Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms.Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism.ConclusionsThis paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.Graphical abstractCDK 2.0 provides new features and improved performance Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0220-4) contains supplementary material, which is available to authorized users.

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Modelling Chemical Reasoning to Predict and Invent Reactions

Cited by 181 publications

References 54 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

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