Judging Chemical Reaction Practicality From Positive Sample only Learning
Table 8: Results for practicality judgment (%)
['[BOLD] Data', '[BOLD] Case', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score', '[BOLD] Accuracy']
Given input sequences, the model will output the reaction success probabilities. To evaluate the result, a threshold is required to distinguish from positive or negative predictions. We observe the positive reactions could be recognized essentially (nearly 99%). Though the proportion of positive and negative cases is over 30:1, our model also ensures a high negative F1-score more than 72%. Besides the rule-based negative dataset have a higher Negative-F1-score. From the statistics of the datasets, we know the rule-based negative dataset is much bigger than the Chemical. AI-Real-1, which not only alleviates the imbalance between positive and negative examples but also increases the diversity of negative examples.