title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð¿ÑÑÐµÐ¹ ÑÐ¸Ð½ÑÐµÐ·Ð° Ñ Ð¸Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ Ð²ÐµÑÐµÑÑÐ² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of modifications of semi-template based models for predicting possible ways of synthesizing chemicals using deep neural networks
Creators: name: ÐÑÐ¾ÑÐ²Ð¸ÑÐ½Ð¸Ð½, ÐÐ²Ð³ÐµÐ½Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¥Ð¸Ð¼Ð¸Ñ
ÑÐµÑÑÐ¾ÑÐ¸Ð½ÑÐµÐ·
Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ñ Ð¸Ð¼Ð¸Ñ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÑ
retrosynthesis
data analysis
transformers

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¸Ì ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¸Ì ÑÐ°Ð±Ð¾ÑÑ: Â«Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸Ì semi-template based Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¸Ì Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð¿ÑÑÐµÐ¸Ì ÑÐ¸Ð½ÑÐµÐ·Ð° ÑÐ¸Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ Ð²ÐµÑÐµÑÑÐ² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¸ÌÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¸ÌÂ». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÑÐ°ÑÑÐ¸Ñ Ð·Ð°Ð´Ð°ÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð¿ÑÑÐµÐ¸Ì ÑÐµÑÑÐ¾ÑÐ¸Ð½ÑÐµÐ·Ð° ÑÐ¸Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¾ÐµÐ´Ð¸Ð½ÐµÐ½Ð¸Ð¸Ì Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸Ì semi-template based Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð½Ð¾Ð¾Ð±ÑÐ°Ð·Ð¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð³ÐµÐ½ÐµÑÐ¸ÑÑÐµÐ¼ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÑÑ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ: 1. Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¸Ì ÑÐ¸Ð¼Ð¸Ð¸ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¿ÑÑÐµÐ¸Ì ÑÐµÑÑÐ¾ÑÐ¸Ð½ÑÐµÐ·Ð°; 2. ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸Ì, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐ¸ÑÑ ÑÐ°Ð·Ð½Ð¾Ð¾Ð±ÑÐ°Ð·Ð¸Ðµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð´Ð»Ñ semi-template based Ð¼ÐµÑÐ¾Ð´Ð¾Ð²; 3. Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸Ì Ð¼ÐµÑÐ¾Ð´Ð¾Ð².Ð Ð°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¾ÑÐºÑÑÑÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑÂ ÑÐ¸Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐµÐ°ÐºÑÐ¸Ð¸Ì USPTO-50K Ð¸ USPTO-full. ÐÐ° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python 3.7 c Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÑÐ¸ÌÐ¼Ð²Ð¾ÑÐºÐ¾Ð² Pytorch Ð¸ Open-NMT Ð±ÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ semi-template based Ð¼ÐµÑÐ¾Ð´Ð¾Ð² RetroXpert Ð¸ GraphRetro, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ»ÑÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÑÐ¸Ðº Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐ°Ð·Ð½Ð¾Ð¾Ð±ÑÐ°Ð·Ð¸Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¸Ì, Diversity Ð¸ Class Diversity. Ð Ð¸ÑÐ¾Ð³Ðµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸Ì Ñ ÑÐ¾ÑÐºÐ¸ Ð·ÑÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÑÐ¸Ðº. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸Ì Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐ´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´,ÑÑÐ¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Diversity Ð½Ð° 2% Ð¸ Class Diversity 3% Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÐµÑÐ²Ð¾Ð¸Ì Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Diversity Ð½Ð° 3% Ð¸ Class Diversity 1.3% Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð²ÑÐ¾ÑÐ¾Ð¸Ì Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸.
description: The subject of the graduate qualification work: "Development of modifications of semi-template based models for predicting possible ways of synthesizing chemicals using deep neural networks". The given work is devoted to the study of methods that solve the problem of predicting possible pathways for the retrosynthesis of chemical compounds and the development of modifications of semi-template based methods to increase the diversity of results generated by the model.The research set the following goals: 1. analysis of existing methods and approaches of predictive chemistry for solving the problem of predicting retrosynthesis pathways; 2. development of modifications that allow increasing the variety of predictions for semi-template based methods; 3. research about the performance of implemented modifications ofÂ methods. The work was performed using USPTO-50K and USPTO-full openÂ chemical reaction datasets. In the Python 3.7 programming language using the Pytorch and Open-NMT frameworks, modifications were implemented for the semi-template based methods RetroXpert and GraphRetro, which improve the performance of the proposed metrics for assessing the diversity of predictions, Diversity and Class Diversity. As a result, a research of the implemented modifications was carried out in terms of the proposed metrics. Based on the research, it can be concluded that the developed modifications allow increasing the value of the Diversity by 2%Â and Class Diversity 3% with the first modification and Diversity by 3% and Class Diversity 1.3% with the second modification.