**DeepDrug** is a deep learning framework to overcome these shortcomings by using graph convolutional networks to learn the graphical representations of drugs and proteins such as molecular fingerprints and residual structures in order to boost the prediction accuracy.

**Fisher-BRC** is an actor critic algorithm for offline reinforcement learning that encourages the learned policy to stay close to the data, namely parameterizing the critic as the $\log$-behavior-policy, which generated the offline dataset, plus a state-action value offset term, which can be learned using a neural network. Behavior regularization then corresponds to an appropriate regularizer on the offset term. A gradient penalty regularizer is used for the offset term, which is equivalent to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature.

Fisher-BRC

Offline Reinforcement Learning with Fisher Divergence Critic Regularization

DeepDrug

DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction

The **Convolutional vision Transformer (CvT)** is an architecture which incorporates convolutions into the [Transformer](https://paperswithcode.com/method/transformer). The CvT design introduces convolutions to two core sections of the ViT architecture.

First, the Transformers are partitioned into multiple stages that form a hierarchical structure of Transformers. The beginning of each stage consists of a convolutional token embedding that performs an overlapping [convolution](https://paperswithcode.com/method/convolution) operation with stride on a 2D-reshaped token map (i.e., reshaping flattened token sequences back to the spatial grid), followed by [layer normalization](https://paperswithcode.com/method/layer-normalization). This allows the model to not only capture local information, but also progressively decrease the sequence length while simultaneously increasing the dimension of token features across stages, achieving spatial downsampling while concurrently increasing the number of feature maps, as is performed in CNNs. 

Second, the linear projection prior to every self-attention block in the Transformer module is replaced with a proposed convolutional projection, which employs a s × s depth-wise separable convolution operation on an 2D-reshaped token map. This allows the model to further capture local spatial context and reduce semantic ambiguity in the attention mechanism. It also permits management of computational complexity, as the stride of convolution can be used to subsample the key and value matrices to improve efficiency by 4× or more, with minimal degradation of performance.

Source	DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com