Chemistry Innovation Center
Chemistry Innovation Center
Schmidt T.C.,Astrazeneca |
Eriksson P.-O.,Astrazeneca |
Gustafsson D.,Emeriti Pharma AB |
Cosgrove D.,Chemistry Innovation Center |
And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2017
Inhibition of plasmin has been found to effectively reduce fibrinolysis and to avoid hemorrhage. This can be achieved by addressing its kringle 1 domain with the known drug and lysine analogue tranexamic acid. Guided by shape similarities toward a previously discovered lead compound, 5-(4-piperidyl)isoxazol-3-ol, a set of 16 structurally similar compounds was assembled and investigated. Successfully, in vitro measurements revealed one compound, 5-(4-piperidyl)isothiazol-3-ol, superior in potency compared to the initial lead. Furthermore, a strikingly high correlation (R2 = 0.93) between anti-fibrinolytic activity and kringle 1 binding affinity provided strong support for the hypothesized inhibition mechanism, as well as revealing opportunities to fine-tune biological effects through minor structural modifications. Several different ligand-based (Freeform, shape, and electrostatic-based similarities) and structure-based methods (e.g., Posit, MM/GBSA, FEP+) were used to retrospectively predict the binding affinities. A combined method, molecular alignment using Posit and scoring with Tcombo, lead to the highest coefficient of determination (R2 = 0.6). © 2017 American Chemical Society.
Chen H.,Chemistry Innovation Center |
Carlsson L.,Computational Toxicology |
Eriksson M.,Chemistry Innovation Center |
Varkonyi P.,Chemistry Innovation Center |
And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2013
A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project. © 2013 American Chemical Society.
Akhondi S.A.,Erasmus Medical Center |
Klenner A.G.,Fraunhofer Institute for Algorithms and Scientific Computing |
Klenner A.G.,European Patent office |
Tyrchan C.,RIA Medicinal Chemistry |
And 9 more authors.
PLoS ONE | Year: 2014
Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org. Copyright: © 2014 Akhondi et al.