Рақамли технологияларнинг назарий ва амалий масалалари Ҷилди 8 № 3 (2025) · Саҳифаҳои 110-122
Computer Drug Development: from Traditional Modeling Methods to Language Models and Quantum Computing
Адилова, Ф.Т., Давронов, Р.Р.
Аннотатсия
Drug development is a central topic at the intersection of structural biology, biochemistry, and medicine, associated with significant challenges such as high cost (billions of dollars), low success rates (less than 10%), and extremely long development cycles (10-15 years). The Computer Aided Drug Design (CADD) system demonstrates tremendous advantages in solving these tasks and speeding up the process, making it an indispensable tool in the pharmaceutical industry and scientific research. The recent development of AlphaFold2 and AlphaFold3, the 2024 Nobel Prize winners, marks significant progress in the field of CADD. In addition to AlphaFold, various machine learning (ML) methods are revolutionizing various stages of drug development, from virtual screening to predictive modeling of drug interactions and treatment targets. Language models such as GPT models offer promising applications for developing research hypotheses and helping to interpret complex biological data. Quantum computing has the potential to solve complex molecular modeling and optimization problems that are currently unsolvable for classical computers, although their practical implementation is still in its early stages. This analytical review presents the latest developments in this field and evaluates the possibilities presented by machine learning, language models and quantum computing in CADD.
компьютерная разработка лекарствмашинное обучениеглубокое обучениеквантовые вычисленияязыковые моделиколичественный анализ взаимосвязи структура-активностьпоиск лекарствмолекулярное моделированиепредсказание структуры белкавиртуальный скрининг
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