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Advanced artificial neural network approaches to prediction of pharmacokinetic properties and toxicity of drug compoundsтезисы доклада
- Авторы: Radchenko E.V., Palyulin V.A., Zefirov N.S.
- Сборник: WATOC 2017 11th Triennial Congress of the World Association of Theoretical and Computational Chemists, Munich, Germany, 27 August – 1 September 2017
- Тезисы
- Год издания: 2017
- Место издания: Munich, Germany
- Первая страница: PO3-93
- Аннотация: The pharmacokinetic properties and toxicity of potential drug compounds (ADMET properties: absorption, distribution, metabolism, excretion, toxicity) critically affect their efficacy, pharmacological profile, administration protocol and safety. Thus, the optimization of these properties is an important aspect of drug discovery and development, and the ability to predict them for new structures can substantially improve the speed and efficiency of such optimization. The artificial neural networks are a powerful nonlinear machine learning method applicable to a broad range of chemistry problems, including the QSAR/QSPR modeling. We have developed a general methodology for the prediction of ADMET parameters based on the application of artificial neural networks and fragmental descriptors to extensive and verified experimental data sets. The fragmental descriptors for a structure are the occurrence counts of the paths, cycles and branches of varied size using an hierarchical atom type classification, providing a ‘holographic’ representation of a molecule. During the model construction, the GPU-based deep learning and double cross-validation are used to achieve optimal performance and model predictivity. During the prediction, a graphic map highlighting the parts of a molecule that make positive or negative contributions to the predicted property is generated as an additional guidance for the ADMET optimization. The models built by us are implemented in an integrated online service available on the Internet (http://qsar.chem.msu.ru/admet/). It supports convenient prediction of important properties (in particular, lipophilicity, blood-brain barrier permeability [1], human intestinal absorption [2], plasma protein binding, mutagenicity, hERG-mediated cardiac toxicity [3], aromatic hydrocarbon receptor binding, cytotoxicity, etc.) as well as qualitative and semi-quantitative estimation of their suitability for drug-like compounds. This integrated prediction system may be used in the research in various areas of medicinal chemistry and pharmacology.
- Добавил в систему: Радченко Евгений Валерьевич