Компьютерная разработка лекарств: от традиционных методов моделирования к языковым моделям и вантовым вычислениям
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Аннотация
Разработка лекарств - центральная тема на стыке структурной биологии, биохимии и медицины, связанная со значительными проблемами, такими как высокая стоимость (миллиарды долларов), низкие показатели успеха (менее 10%) и чрезвычайно длительные циклы разработки (10-15лет). Система автоматизированного поиска лекарств (Computer Aided Drug Design, CADD) демонстрирует огромные преимущества в решении этих задач и ускорении процесса, что делает ее незаменимым инструментом в фармацевтической промышленности и научных исследованиях. Недавняя разработка AlphaFold2 и AlphaFold3,- лауреатов Нобелевской премии 2024г., знаменует собой значительный прогресс в области CADD. Помимо AlphaFold, различные методы машинного обучения (ML) революционизируют различные этапы разработки лекарств - от виртуального скрининга до прогнозирующего моделирования взаимодействий между лекарством и таргетами лечения. Языковые модели, такие как GPT-модели, предлагают многообещающие приложения для выработки исследовательских гипотез и помощи в интерпретации сложных биологических данных. Квантовые вычисления обладают потенциалом для решения сложных задач молекулярного моделирования и оптимизации, которые в настоящее время неразрешимы для классических компьютеров, хотя их практическая реализация все еще находится на ранних стадиях. В данном аналитическом обзоре представлены новейшие достижения в этой области и оценены возможности, представляемые машинным обучением, языковыми моделями и квантовыми вычислениями в CADD.
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