Deep learning is accelerating drug discovery. However, current approaches are often
affected by limitations in the available data, in terms of either size or molecular diversity …

Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution
system (DWDS) remains challenging. Predicting DBPs using readily available water quality …

Bioenergy offers a sustainable alternative to fossil fuels, addressing energy security and
climate change concerns. This paper reviews the current landscape of machine learning …

The recovery of valuable minerals in froth flotation industry relies on finding inexpensive and
environmentally friendly reagents that selectively adsorb upon surfaces and interfaces …

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods
for molecular property prediction. However, a key limitation of typical GNN models is their …

Animal experiments traditionally identify sensitizers in cosmetic materials. However, with
growing concerns over animal ethics and bans on such experiments globally, alternative …

Deep learning is accelerating drug discovery. However, current approaches are often
affected by limitations in the available data, eg, in terms of size or molecular diversity. Active …

Deep learning holds great potential for expediting the discovery of new polymers from the
vast chemical space. However, accurately predicting polymer properties for practical …

The transformative impact of modern computational paradigms and technologies, such as
high-performance computing (HPC), quantum computing, and cloud computing, has opened …

Uncertainty quantification is emerging as a critical tool in high-stakes decision-making
processes, where trust in automated predictions that lack accuracy and precision can be …