Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

The design of molecules requires multi-objective optimizations in high-dimensional
chemical space with often conflicting target properties. To navigate this space, classical …

Effective transfer learning for molecular property prediction has shown considerable strength
in addressing insufficient labeled molecules. Many existing methods either disregard the …

Early-stage drug discovery has been limited by initial hit identification and lead optimization
and their associated costs. Ultra-large virtual screens (ULVSs), which involve the virtual …

It is a time-consuming and costly process to develop affordable and high-performance
organic photovoltaic materials. Computational methods are essential for accelerating the …

Molecular relational learning (MRL) is crucial for understanding the interaction behaviors
between molecular pairs, a critical aspect of drug discovery and development. However, the …

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse
molecular design. However, GAs have multiple hyperparameters that have not been …

The discovery of small molecules with therapeutic potential is a long-standing challenge in
chemistry and biology. Researchers have increasingly leveraged novel computational …

Efficiently retrieving an enormous chemical library to design targeted molecules is crucial for
accelerating drug discovery, organic chemistry, and optoelectronic materials. Despite the …

Deep generative models have emerged as a popular machine learning-based approach for
inverse design problems in the life sciences. However, these problems often require …