Exploring Chemoinformatics Aspects of Few-Shot Meta-Learning by Example of an Infinite Dilution Activity Coefficient in Ionic Liquid Prediction

, RMSE, and MAE indicate comparable performance to graph neural networks, even when trained on only 64 or 128 data points. The versatility of the fine-tuned models suggests that, in certain cases, performance on a single task may be achieved at the expense of reduced model versatility (catastrophic forgetting). The similarity between the test and training tasks (as approximated by Tanimoto similarity of the solutes' molecules) was identified as a factor affecting performance on the test task. Consequently, Task Similarity-Aware Reptile (TSA-Reptile) was proposed to target those dissimilar tasks. This novel method scales the loss function by similarity to the closest training task. It was shown to outperform MAML on out-of-distribution tasks. Beyond providing the comparative analysis of meta-learning and traditional deep learning, potential strengths of both MAML and TSA-Reptile are discussed.