Highlights•Inactivity data is helpful.•Structure-Inactivity Relationships (SIRs) are valuable in
drug discovery.•Machine and deep learning benefit from SIRs.•The inactivity data gap in the …

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that
large numbers of literature-reported examples should enable construction of accurate and …

Pressure injuries are increasing worldwide, and there has been no significant improvement
in preventing them. This study is aimed at reviewing and evaluating the studies related to the …

Intrusion detection is an effective means of ensuring the proper functioning of industrial
control systems (ICSs). Most intrusion detection algorithms learn the historical ICS data to …

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling
the rapid assessment of compound properties before costly and time-consuming laboratory …

Featured Application The application of “Autonomous Navigation of Robots: Optimization
with DQN” involves using reinforcement learning techniques to optimize the navigation of …

For durian growth to produce high-quality fruit, plants should receive sufficient nutrients.
Currently, farmers apply various fertilisers to produce a large quantity and quality of durian …

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology,
and concerted efforts are underway to establish its applicability to various industries …

Quantitative structure− activity relationship (QSAR) models are powerful in silico tools for
predicting the mutagenicity of unstable compounds, impurities and metabolites that are …

Skin irritation test is an essential part of the safety assessment of chemicals. Recently,
computational models to predict the skin irritation draw attention as alternatives to animal …