Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …

Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

Recently, pre-trained foundation models have enabled significant advancements in multiple
fields. In molecular machine learning, however, where datasets are often hand-curated, and …

We investigate the computational limits of the memory retrieval dynamics of modern Hopfield
models from the fine-grained complexity analysis. Our key contribution is the …

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series
prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a …

We introduce an Outlier-Efficient Modern Hopfield Model (termed $\mathtt {OutEffHop} $)
and use it to address the outlier-induced challenge of quantizing gigantic transformer-based …

In-context learning has become an important approach for few-shot learning in Large
Language Models because of its ability to rapidly adapt to new tasks without fine-tuning …

We present a nonparametric construction for deep learning compatible modern Hopfield
models and utilize this framework to debut an efficient variant. Our key contribution stems …

Computational approaches have revolutionized the field of drug discovery, collectively
known as Computer-Assisted Drug Design (CADD). Advancements in computing power …