We present a novel differentiable ODE solving infrastructure that enables efficient computation of gradients through ODE solvers. Our implementation demonstrates significant improvements in both computational efficiency and numerical stability compared to existing approaches.
This work explores various deep learning architectures for predicting molecular properties, with a focus on interpretability and uncertainty quantification. We propose a novel attention mechanism that significantly improves prediction accuracy.
We introduce a scalable neural architecture specifically designed for drug discovery tasks. The model achieves state-of-the-art results while being significantly more computationally efficient than existing approaches.