Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular representations that effectively generalize in real-world scenarios to novel compounds remains an elusive and unresolved task. This study examines how atom-level pretraining with quantum mechanics (QM) data can mitigate violations of assumptions regarding the distributional similarity between training and test data and therefore improve performance and generalization in downstream tasks. In the public dataset Therapeutics Data Commons (TDC), we show how pretraining on atom-level QM improves performance overall and makes the activation of the features distributes more Gaussian-like which results in a representation that is more robust to distribution shifts. To the best of our knowledge, this is the first time that hidden state molecular representations are analyzed to compare the effects of molecule-level and atom-level pretraining on QM data.

We generated 25000 conversations labeled with Big Five Personality traits using prompt programming at GPT-3. Then we train Big Five classification models with these data and evaluate them with 2500 data from generated dialogues and real conversational datasets labeled in Big Five by human annotators. The results indicated that this approach is promising for creating effective training data. We then compare the performance by different training approaches and models. Our results suggest that using Adapter-Transformers and transfer learning from pre-trained RoBERTa sentiment analysis model will perform best with the generated data. Our best model obtained an accuracy of 0.71 in generated data and 0.65 in real datasets. Finally, we discuss this approach's potential limitations and confidence metric.