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SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

ABSTRACT: We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

SUBMITTER: Patel H

PROVIDER: S-EPMC7658252 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

Patel Hitesh H Ihlenfeldt Wolf-Dietrich WD Judson Philip N PN Moroz Yurii S YS Pevzner Yuri Y Peach Megan L ML Delannée Victorien V Tarasova Nadya I NI Nicklaus Marc C MC

Scientific data 20201111 1

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building ...[more]

PMID: 33177514

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Project description:BackgroundElectronic health (eHealth) usability evaluations of rapidly developed eHealth systems are difficult to accomplish because traditional usability evaluation methods require substantial time in preparation and implementation. This illustrates the growing need for fast, flexible, and cost-effective methods to evaluate the usability of eHealth systems. To address this demand, the present study systematically identified and expert-validated rapidly deployable eHealth usability evaluation methods.ObjectiveIdentification and prioritization of eHealth usability evaluation methods suitable for agile, easily applicable, and useful eHealth usability evaluations.MethodsThe study design comprised a systematic iterative approach in which expert knowledge was contrasted with findings from literature. Forty-three eHealth usability evaluation methods were systematically identified and assessed regarding their ease of applicability and usefulness through semi-structured expert interviews with 10 European usability experts and systematic literature research. The most appropriate eHealth usability evaluation methods were selected stepwise based on the experts' judgements of their ease of applicability and usefulness.ResultsOf these 43 eHealth usability evaluation methods identified as suitable for agile, easily applicable, and useful eHealth usability evaluations, 10 were recommended by the experts based on their usefulness for rapid eHealth usability evaluations. The three most frequently recommended eHealth usability evaluation methods were Remote User Testing, Expert Review, and Rapid Iterative Test and Evaluation Method. Eleven usability evaluation methods, such as Retrospective Testing, were not recommended for use in rapid eHealth usability evaluations.ConclusionWe conducted a systematic review and expert-validation to identify rapidly deployable eHealth usability evaluation methods. The comprehensive and evidence-based prioritization of eHealth usability evaluation methods supports faster usability evaluations, and so contributes to the ease-of-use of emerging eHealth systems.

Project description:Recent advances in the development of small chemical compounds that can modulate RNA splicing brought excitement to the field of splicing-targeting therapy. Splicing-targeting therapy tries to ameliorate the disease by altering the exon combination of transcripts to reduce the undesired effect of genetic mutations. However, the knowledge and tools to understand factors contributing to splicing modulator compound sensitivity have been lacking. Our goal was to establish a method to characterize sequence features found in compound sensitive exons.Here we developed a comparative transcriptomic approach to explore features that make an exon sensitive to a chemical compound. In this study, we chose TG003, a potential drug for Duchenne muscular dystrophy, and performed RNA-sequencing on samples from human and mouse skeletal muscle cells, with and without TG003 treatments. We compared TG003 responsiveness between homologous exon pairs and identified 21 pairs in which human exons were skip-enhanced but not mouse exons. We compared the sequence features; splice site scores, number of splicing factor binding sites, and properties of branch sequence and polypyrimidine tracts, and found that polypyrimidine tracts were stronger (longer stretches and richer content of consecutive polypyrimidine) in the mouse TG003 insensitive exons. We also compared the features between TG003 skip-enhanced and insensitive exons within the species, and discovered that human TG003 skip-enhanced exons were shorter and had less splicing factor binding sites than the group of human TG003 insensitive exons. Mouse insensitive exons homologous to human TG003 skip-enhanced exons shared these properties. Our results suggested that these features are prerequisites for TG003 skip-enhanced exons and weak polypyrimidine tracts are defining features, which were supported by a decision tree analysis on all cassette exons in human.In this study we established a comparative transcriptomic approach, which shed lights on how small chemical compounds modulate RNA splicing. The results described here was the first attempt to decipher the targeting rules of a splicing modulator compound. We expect that this approach would contribute to the precise understanding of the mechanism of TG003-induced splicing modulation, expand target diseases of splicing modulators in general, as well as the development of new splicing modulators.

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SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

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SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

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