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Inverse QSAR: Reversing Descriptor-Driven Prediction Pipeline Using Attention-Based Conditional Variational Autoencoderстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Bort William, Mazitov Daniyar, Horvath Dragos, Bonachera Fanny, Lin Arkadii, Marcou Gilles, Baskin Igor, Madzhidov Timur, Varnek Alexandre
- Журнал: Journal of Chemical Information and Modeling
- Том: 62
- Номер: 22
- Год издания: 2022
- Издательство: American Chemical Society
- Местоположение издательства: United States
- Первая страница: 5471
- Последняя страница: 5484
- DOI: 10.1021/acs.jcim.2c01086
- Аннотация: In order to better foramize it, the notorious inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process including (i) finding “seed” descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the “seed” vectors. The main development effort here was focused on the latter stage, proposing a new attention-based conditional variational autoencoder neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as “orthogonal” in silico validation methods, proving that some of de novo structures are, beyond being predicted active by 2D-QSAR models, clearly able to match binding 3D pharmacophores and bind the protein pocket.
- Добавил в систему: Баскин Игорь Иосифович