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 …

Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative
structure-activity/property (QSAR/QSPR) relationships and classification. However, the size …

Abstract Molecular Property Prediction (MPP) is a crucial task in the AI-driven Drug
Discovery (AIDD) pipeline, which has recently gained considerable attention thanks to …

This study aims to quantify the combustion performances, mechanisms, and ash
characteristics of Chinese medicine residues (CMR) in the air and oxy-fuel atmospheres …

Computational methods in medicinal chemistry facilitate drug discovery and design. In
particular, machine learning methodologies have recently gained increasing attention. This …

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due
to their potential to interfere with nuclear receptors (NRs), key regulators of physiological …

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of
the most important targets for drug discovery. Current computational strategies mainly focus …

Neural networks are rapidly gaining popularity in chemical modeling and Quantitative
Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems …

Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which
has recently gained considerable attention thanks to advances in deep neural networks …