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- Johansson, Simon, 1994, et al.
(author)
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AI-assisted synthesis prediction
- 2019
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In: Drug Discovery Today: Technologies. - : Elsevier BV. - 1740-6749. ; 32-33:December, s. 65-72
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Journal article (peer-reviewed)abstract
- Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper. © 2020 Elsevier Ltd
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- Varadharajan, S., et al.
(author)
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Exploring In Silico Prediction of the Unbound Brain-to-Plasma Drug Concentration Ratio: Model Validation, Renewal, and Interpretation
- 2015
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In: Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0022-3549. ; 104:3, s. 1197-1206
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Journal article (peer-reviewed)abstract
- Recently, we built an in silico model to predict the unbound brain-to-plasma concentration ratio (K-p,K-uu,K-brain), a measure of the distribution of a compound between the blood plasma and the brain. Here, we validate the previous model with new additional data points expanding the chemical space and use that data also to renew the model. The model building process was similar to our previous approach; however, a new set of descriptors, molecular signatures, was included to facilitate the model interpretation from a structure perspective. The best consensus model shows better predictive power than the previous model (R-2 = 0.6 vs. R-2 = 0.53, when the same 99 compounds were used as test set). The two-class classification accuracy increased from 76% using the previous model to 81%. Furthermore, the atom-summarized gradient based on molecular signature descriptors was proposed as an interesting new approach to interpret the K-p,K-uu,K-brain machine learning model and scrutinize structure K-p,K-uu,K-brain relationships for investigated compounds. (c) 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:1197-1206, 2015
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- Wimberger, Sandra, 1987, et al.
(author)
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Simultaneous inhibition of DNA-PK and Pol ϴ improves integration efficiency and precision of genome editing
- 2023
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In: Nature Communications. ; 14:1
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Journal article (peer-reviewed)abstract
- Genome editing, specifically CRISPR/Cas9 technology, has revolutionized biomedical research and offers potential cures for genetic diseases. Despite rapid progress, low efficiency of targeted DNA integration and generation of unintended mutations represent major limitations for genome editing applications caused by the interplay with DNA double-strand break repair pathways. To address this, we conduct a large-scale compound library screen to identify targets for enhancing targeted genome insertions. Our study reveals DNA-dependent protein kinase (DNA-PK) as the most effective target to improve CRISPR/Cas9-mediated insertions, confirming previous findings. We extensively characterize AZD7648, a selective DNA-PK inhibitor, and find it to significantly enhance precise gene editing. We further improve integration efficiency and precision by inhibiting DNA polymerase theta (Pol ϴ). The combined treatment, named 2iHDR, boosts templated insertions to 80% efficiency with minimal unintended insertions and deletions. Notably, 2iHDR also reduces off-target effects of Cas9, greatly enhancing the fidelity and performance of CRISPR/Cas9 gene editing. Low efficiency of target DNA integration remains a challenge in genome engineering. Here the authors perform large-scale compound library and genetic screens to identify targets that enhance gene editing: they see that combined DNA-PK and Pol ϴ inhibition with potent compounds increases editing efficiency and precision.
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