Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our
approach to complex data. This review explores the attention mechanism and its extended …

Molecular property prediction has gained significant attention due to its transformative
potential in multiple scientific disciplines. Conventionally, a molecule graph can be …

Transformer-based large language models have remarkable potential to accelerate design
optimization for applications such as drug development and material discovery. Self …

This paper is dedicated to the quantum chemical package Jaguar, which is commercial
software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific …

We consider molecule generation in 3D space using language models (LMs), which
requires discrete tokenization of 3D molecular geometries. Although tokenization of …

Large Language Models (LLMs) can be used as repositories of biological and chemical
information to generate pharmacological lead compounds. However, for LLMs to focus on …

Abstract Machine learning models have found numerous successful applications in
computational drug discovery. A large body of these models represents molecules as …

Drug discovery is aimed to design novel molecules with specific chemical properties for the
treatment of targeting diseases. Generally, molecular optimization is one important step in …

AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …