  Virtual screening (VS) is a critical step in computer-aided drug discovery,
aiming to identify molecules that bind to a specific target receptor like
protein. Traditional VS methods, such as docking, are often too time-consuming
for screening large-scale molecular databases. Recent advances in deep learning
have demonstrated that learning vector representations for both proteins and
molecules using contrastive learning can outperform traditional docking
methods. However, given that target databases often contain billions of
molecules, real-valued vector representations adopted by existing methods can
still incur significant memory and time costs in VS. To address this problem,
in this paper we propose a hashing-based contrastive learning method, called
DrugHash, for VS. DrugHash treats VS as a retrieval task that uses efficient
binary hash codes for retrieval. In particular, DrugHash designs a simple yet
effective hashing strategy to enable end-to-end learning of binary hash codes
for both protein and molecule modalities, which can dramatically reduce the
memory and time costs with higher accuracy compared with existing methods.
Experimental results show that DrugHash can outperform existing methods to
achieve state-of-the-art accuracy, with a memory saving of 32$\times$ and a
speed improvement of 3.5$\times$.
