Artificial intelligence-based methods can significantly improve the traditional expen sive drug development process, given the fact that various generative models are already widely used in chemistry. Generative models for de novo drug design are focused on cre ating new biological compounds completely from scratch, which represents a promising direction in the future. The rapid development in this field, combined with the inherent complexity of the drug design process, creates difficult conditions for researchers. Within the framework of the topic of creating small molecules, we define many subtasks and applications, highlighting important datasets, benchmarks, model architecture and com paring the performance of the best models. The review presents key advances in this f ield, including the advent of quantum computing, which promises to further accelerate the application of deep QSAR to support computer-aided drug design in the field of molecules.
| Mualliflar | Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., Давронов, Р.Р. |
|---|---|
| Jurnal | Ҳисоблаш ва амалий математика муаммолари |
| Nashr sanasi | 2024-05-22 |
| Son | 2 |
| Betlar | 85-98 |
| Til | Rus |
generative models, biological compounds, small molecules, datasets, bench marks, model architecture, quantum computing, QSAR, генеративные модели, биологические соединения, малые молекулы, наборы данных, контрольные показатели, архитектура моделей, квантовые вычисления, QSAR
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