Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design)

Adilova, F.T., Davronov, R.R., Адылова, Ф.Т., Давронов, Р.Р.

Ҳисоблаш ва амалий математика муаммолари · 2026-yil

Annotatsiya

The discovery of new chemical compounds with specified properties is a challenging problem in drug development. Many studies encode molecules as string representations derived from molecular graphs; however, this approach is computationally expensive and does not readily extend to general (non-molecular) graphs. Advances in graph deep learning make it possible to train generative models directly on graph representations, avoiding costly search in the discrete and extremely large space of chemical structures. MolGAN is a family of implicit models for generating small molecular graphs that combines generative adversarial networks (GANs) with reinforcement learning (RL) to produce molecules with target chemical properties. This review considers four variants: the baseline MolGAN (up to 9 atoms), Large MolGAN (up to 20 atoms) with a graph-expansion mechanism that reduces the generation of disconnected graphs, a MolGAN variant based on WGAN as a more stable alternative to standard GAN training, and a hybrid MolGAN incorporating quantum computing modules. The paper describes the model architectures, compares their performance on established benchmarks, and discusses limitations and directions for future research.

Maqola ma’lumotlari
MualliflarAdilova, F.T., Davronov, R.R., Адылова, Ф.Т., Давронов, Р.Р.
JurnalҲисоблаш ва амалий математика муаммолари
NashriyotТошкент ахборот технологиялари университети қошидаги, Ахборот-коммуникация технологиялари илмий-инновацион, маркази
ISSN2181-8460, 2181-046X
Nashr sanasi2026-03-07
Son1
Betlar123-142
TilIngliz
Ilmiy sohaFizika-matematika
DOI10.71310/pcam.1_71.2026.11
Indeksga qo‘shilgan2026-08-27

DOI: 10.71310/pcam.1_71.2026.11 · Maqolaning asl sahifasi

Kalit so‘zlar

reinforcement learning, WGAN, implicit generative models, graph connectivity enforcement, quantum–classical hybrid models, molecular design, molecule generation benchmarks, обучение с подкреплением, WGAN, неявные генеративные модели, обеспечение связности графов, квантово-классические гибридные модели, молекулярный дизайн, бенчмарки генерации молекул

Ilmiy soha

Fizika-matematika

Iqtibos keltirish

APAAdilova, F.T., Davronov, R.R., Адылова, Ф.Т., & Давронов, Р.Р. (2026). Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design). Ҳисоблаш ва амалий математика муаммолари, (1), 123-142. https://doi.org/10.71310/pcam.1_71.2026.11
GOST (OAK)Adilova, F.T. va b. Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design) // Ҳисоблаш ва амалий математика муаммолари. – 2026. – № 1. – P. 123-142. – DOI: 10.71310/pcam.1_71.2026.11.
MLAAdilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р. “Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design).” Ҳисоблаш ва амалий математика муаммолари, (1), 123-142, 2026. https://doi.org/10.71310/pcam.1_71.2026.11
ChicagoAdilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р. “Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design).” Ҳисоблаш ва амалий математика муаммолари, (1), 123-142 (2026). https://doi.org/10.71310/pcam.1_71.2026.11
HarvardAdilova, F.T., Davronov, R.R., Адылова, Ф.Т., and Давронов, Р.Р (2026) ‘Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design)’, Ҳисоблаш ва амалий математика муаммолари, (1), 123-142. https://doi.org/10.71310/pcam.1_71.2026.11
BibTeX
@article{adilovaft2026,
  title = {Graph Generation with a Prescribed Structure: From Deep Neural Networks to Quantum Models (A Case Study of Novel Drug Design)},
  author = {Adilova, F.T. and Davronov, R.R. and Адылова, Ф.Т. and Давронов, Р.Р.},
  journal = {Ҳисоблаш ва амалий математика муаммолари},
  year = {2026},
  number = {1},
  pages = {123-142},
  doi = {10.71310/pcam.1_71.2026.11},
  url = {https://doi.org/10.71310/pcam.1_71.2026.11}
}

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