Dataset Information

Cocrystals of Praziquantel: Discovery by Network-Based Link Prediction.

ABSTRACT: Cocrystallization has been promoted as an attractive early development tool as it can change the physicochemical properties of a target compound and possibly enable the purification of single enantiomers from racemic compounds. In general, the identification of adequate cocrystallization candidates (or coformers) is troublesome and hampers the exploration of the solid-state landscape. For this reason, several computational tools have been introduced over the last two decades. In this study, cocrystals of Praziquantel (PZQ), an anthelmintic drug used to treat schistosomiasis, are predicted with network-based link prediction and experimentally explored. Single crystals of 12 experimental cocrystal indications were grown and subjected to a structural analysis with single-crystal X-ray diffraction. This case study illustrates the power of the link-prediction approach and its ability to suggest a diverse set of new coformer candidates for a target compound when starting from only a limited number of known cocrystals.

SUBMITTER: Devogelaer JJ

PROVIDER: S-EPMC8276530 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Cocrystals of Praziquantel: Discovery by Network-Based Link Prediction.

Devogelaer Jan-Joris JJ Charpentier Maxime D MD Tijink Arnoud A Dupray Valérie V Coquerel Gérard G Johnston Karen K Meekes Hugo H Tinnemans Paul P Vlieg Elias E Ter Horst Joop H JH de Gelder René R

Crystal growth & design 20210520 6

Cocrystallization has been promoted as an attractive early development tool as it can change the physicochemical properties of a target compound and possibly enable the purification of single enantiomers from racemic compounds. In general, the identification of adequate cocrystallization candidates (or coformers) is troublesome and hampers the exploration of the solid-state landscape. For this reason, several computational tools have been introduced over the last two decades. In this study, cocr ...[more]

PMID: 34276256

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Project description:As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's topological and dynamical attributes, network representation has been a prevalent technique, which can map a network or substructures (like nodes) into a low-dimensional vector space. Since its mainstream methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, the learned vector's dimension is indeterminable and the elements are not interpreted. Although massive efforts to cope with this issue have included, say, automated machine learning by computer scientists and learning theory by mathematicians, the root causes still remain unresolved. Consequently, enterprises need to spend enormous computing resources to work out a set of model hyperparameters that can bring good performance, and business personnel still finds difficulties in explaining the learned vector's practical meaning. Given that, from a physical perspective, this article proposes two determinable and interpretable node representation methods. To evaluate their effectiveness and generalization, this article proposes Adaptive and Interpretable ProbS (AIProbS), a network-based model that can utilize node representations for link prediction. Experimental results showed that the AIProbS can reach state-of-the-art precision beyond baseline models on some small data whose distribution of training and test sets is usually not unified enough for machine learning methods to perform well. Besides, it can make a good trade-off with machine learning methods on precision, determinacy (or robustness), and interpretability. In practice, this work contributes to industrial companies without enough computing resources but who pursue good results based on small data during their early stage of development and who require high interpretability to better understand and carry out their business.

Dataset Information

Cocrystals of Praziquantel: Discovery by Network-Based Link Prediction.

Publications

Cocrystals of Praziquantel: Discovery by Network-Based Link Prediction.

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