We developed a Guideline Implementation Planning Checklist by conducting a comprehensive review of existing resources. This can be applied by guideline developers or users to prepare for and/or undertake implementation. Information was variable across documents, and, in general, quite sparse. In large part this was due to the fact that most resources focused on guideline development, and few were dedicated to guideline implementation. As a result, the Guideline Implementation Planning Checklist is brief. However, it provides developers or users with a framework for implementation planning that coincides with guideline development, can be adapted to context-specific factors and therefore may be broadly applicable, can be employed by guideline developers or users so its application is flexible and, as a synthesis of available information, it is more detailed than existing resources.
Elements of Synthesis Planning download pdf
We appraised and abstracted include articles in a three-step process. First, each article was read by a primary reviewer who wrote a narrative synopsis using a template (see Additional File 1, Synopsis template). The purpose of the initial synopsis was to provide an overall summary and critique of the article. Second, the completed synopsis was distributed and reviewed by all co-authors, and discussed and refined on a conference call. Third, one of the co-authors condensed each synopsis using a structured summary table, with a separate table for each article. The purpose of the summary tables was to create a concise, structured appraisal and critique for each article. Some papers were empirical and others were conceptual. Summary tables for empirical articles included the overall method/design, an appraisal of study quality, study outcomes, how PARIHS was proposed to be used and actually used, and assessment of congruency between PARIHS and study methods (see Additional File 2, Empirical article summary table). These tables also listed how PARIHS elements and sub-elements were defined and measured or operationalized in the study, along with findings, barriers, and enablers to implementation. The summary tables for core concept articles focused on the framework's elements, sub-elements, limitations, recommendations, and other observations (Additional File 3, Core-concept article summary table). These summary tables were reviewed by the primary reviewer for that paper and again by all co-authors, discussed as a group, and affirmed or revised as needed. This collection of empirical and core summary tables constituted the analytic foundation for our meta-summary and synthesis.
Four co-authors reviewed the final set of summary tables and independently highlighted key points per article to create a meta-summary. Key points represented concepts, specific findings related to PARIHS generally and/or to specific elements or sub-elements, observations about the use of the framework, and conclusions. Information highlighted as a key point by at least three of the four co-authors was discussed further at a two-day, in-person working conference. The purpose of the discussion of key points was to explore and summarize similarities and differences across the papers (both empirical and core conceptual) and to develop qualitative themes. Some of the themes were descriptive, e.g., regarding the actual versus articulated use of PARIHS. Other themes were interpretive, e.g., our consensus judgments regarding overall limitations, related issues, and strengths of the framework relative to the ability of researchers to effectively use it to guide an implementation study. We developed implications for using the framework as well as related recommendations based on these synthesized findings. As with the article appraisal, the synthesis and recommendations were discussed with all co-authors and refined until consensus was reached.
Additional file 3: Summary table template for core concept articles. The summary table template is a semi-structured tool for article abstraction and critique that was in tabular format and included more discrete data elements than the synopsis template, e.g., broken down by PARIHS element and sub-element synthesis. The summary table differed between the core-concept and empirical articles because of the types of publication and related content (e.g., differences in the purposes and methods of the papers). This is the summary table for the core concept articles. (DOC 79 KB)
Retrosynthesis planning algorithms can be divided into template-based and template-free approaches. In template-based approaches, reaction templates or rules that describe chemical transformations are manually encoded or derived from a database of known reactions, and subsequently applied to other compounds to create plausible reaction outcomes. Segler et al. showed that it was possible to train a neural network to prioritize templates, and subsequently use this as a policy to guide a Monte Carlo tree search algorithm that suggests synthetic pathways for a given compound [7, 8]. Template-free approaches, on the other hand, do not rely on such templates but typically treat the chemical reaction as a natural language problem, where one set of words (reactants) is transformed into another set of words (products) [9,10,11]. Other template-free methods are based on graph approaches [12, 13].
We have presented the AiZynthFinder tool for retrosynthesis planning. In our experience, it can suggest synthetic routes for most compounds in a very short time. We hope that it will be perceived as user-friendly and with a low learning curve, because we provide extensive documentation. Furthermore, the software is robust and flexible and lends itself to easy extension with novel features. Although it does not provide a complete and integrated solution for synthesis planning, we believe that we have provided a framework and platform where novel algorithms can be tested and integrated in the future. We hope that by releasing the software to the public that researchers interested in retrosynthesis can use it to explore synthetic route prediction and provide suggestion how it can be improved. By providing open source code and algorithmic transparency, we aim to promote collaboration around a sustainable reference software. We encourage users to contribute ideas or code so that the tool can be incrementally improved and thereby provide more accurate and useful predictions of reaction routes.
SG managed the refactoring project, refactored and made improvements to the code, developed the testing framework, performed the tool comparisons and wrote the initial manuscript. AT worked with the reaction datasets, extracted the templates and trained and developed the policy networks. VC investigated the performance and feasibility of the synthesis predictions. J-LR was AT academic supervisor and provided helpful feedback and guidance. OE supervised and managed the team. EB designed and coded the first version of the Monte Carlo tree-search software and supervised and managed the project in the early phases. All authors were involved in feedback, planning of the work and editing and improving the manuscript. All authors read and approved the final manuscript.
Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of biocatalysed reactions in retrosynthetic planning clashes with the difficulties in predicting the enzymatic activity on unreported substrates and enzyme-specific stereo- and regioselectivity. As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, we extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis. The enzymatic knowledge is learned from an extensive data set of publicly available biochemical reactions with the aid of a new class token scheme based on the enzyme commission classification number, which captures catalysis patterns among different enzymes belonging to the same hierarchy. The forward reaction prediction model (top-1 accuracy of 49.6%), the retrosynthetic pathway (top-1 single-step round-trip accuracy of 39.6%) and the curated data set are made publicly available to facilitate the adoption of enzymatic catalysis in the design of greener chemistry processes.
Despite the impact on traditional synthetic organic chemistry, computer-aided synthesis planning tools using biocatalytic reactions are in the early days of their development. Currently, only rule-based methods for predicting biosynthesis pathways have been examined, such as the ATLAS of Biochemistry or RetroRules20,21,22. Lately, RetroBioCat23 became the first chemoinformatic approach for easing the adoption of biocatalytic reactions specifically for chemical synthesis. However, the implementation relies on a set of expertly encoded reaction rules coupled with a system for retrieving database records to enable the use of biocatalysis in synthetic organic chemistry. This use of reaction templates slows down the curation of newly collected data, requiring the intervention of human experts, and suffers from the limitation in capturing the effects on the reaction centre of long-range substituents. Shortly after, Kreutter et al.24 presented a forward reaction prediction model based on the Molecular Transformer25. This approach exploits a multitask transfer learning to train a Molecular Transformer architecture, originally trained with chemical reactions from the US Patent Office (USPTO) data set, with 32,000 enzymatic transformations, each one annotated with the corresponding enzyme name. The enzymatic transformer model predicts the products formed from a given substrate and enzyme in the forward prediction task, reaching an accuracy of 54% when using the enzyme name information only and 62% when using the complete enzyme information as a full sentence (often also including the organism name). The approach addresses some of the concerns around scalability and data curation of reaction templates. However, the use of enzyme names as reaction tokens adds an additional level of challenge when trying to learn chemical reactivity patterns among enzymes with different names but belonging to closely related families. In addition, the lack of a corresponding backward model in this work does not allow for retrosynthetic planning. 2ff7e9595c
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