# Judging Chemical Reaction Practicality From Positive Sample only Learning

## TL;DR

This paper introduces a machine learning approach that predicts the practicality of chemical reactions using only positive samples, achieving high accuracy without relying on complex chemistry knowledge.

## Contribution

It proposes an auto-construction method to address the negative sample insufficiency problem in reaction practicality prediction.

## Key findings

- Achieves 99.76% accuracy on large-scale reaction data
- Effectively predicts reaction practicality without negative samples
- Introduces a novel auto-construction method for training data

## Abstract

Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference. Considering that chemical reactions have been represented in a language form, we propose a new solution to generally judge the practicality of organic reaction without considering complex quantum physical modeling or chemistry knowledge. While tackling the practicality judgment as a machine learning task from positive and negative (chemical reaction) samples, all existing studies have to carefully handle the serious insufficiency issue on the negative samples. We propose an auto-construction method to well solve the extensively existed long-term difficulty. Experimental results show our model can effectively predict the practicality of chemical reactions, which achieves a high accuracy of 99.76\% on real large-scale chemical lab reaction practicality judgment.