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 creating 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 |
| Nashriyot | Тошкент ахборот технологиялари университети қошидаги, Ахборот-коммуникация технологиялари илмий-инновацион, маркази |
| ISSN | 2181-8460, 2181-046X |
| Nashr sanasi | 2024-05-22 |
| Son | 2 |
| Betlar | 85-98 |
| Til | Ingliz |
| Ilmiy soha | Fizika-matematika |
| Indeksga qo‘shilgan | 2026-08-27 |
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
| APA | Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., & Давронов, Р.Р. (2024). GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE. Ҳисоблаш ва амалий математика муаммолари, (2), 85-98. https://journals.airi.uz/index.php/pvpm/article/view/16 |
|---|---|
| GOST (OAK) | Adilova, F.T. va b. GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE // Ҳисоблаш ва амалий математика муаммолари. – 2024. – № 2. – P. 85-98. |
| MLA | Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р. “GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE.” Ҳисоблаш ва амалий математика муаммолари, (2), 85-98, 2024. https://journals.airi.uz/index.php/pvpm/article/view/16 |
| Chicago | Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р. “GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE.” Ҳисоблаш ва амалий математика муаммолари, (2), 85-98 (2024). https://journals.airi.uz/index.php/pvpm/article/view/16 |
| Harvard | Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р (2024) ‘GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE’, Ҳисоблаш ва амалий математика муаммолари, (2), 85-98. https://journals.airi.uz/index.php/pvpm/article/view/16 |
| BibTeX | @article{adilovaft2024,
title = {GENERATIVE AI FOR DE NOVO DRUG DESIGN: NEW CHALLENGES IN MOLECULE},
author = {Adilova, F.T. and Davronov, R.R. and Адылова, Ф.Т. and Давронов, Р.Р.},
journal = {Ҳисоблаш ва амалий математика муаммолари},
year = {2024},
number = {2},
pages = {85-98},
url = {https://journals.airi.uz/index.php/pvpm/article/view/16}
} |
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