Artificial intelligence (AI) has the potential to revolutionize the drug discovery process,
offering improved efficiency, accuracy, and speed. However, the successful application of AI …

Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …

In silico models based on Deep Neural Networks (DNNs) are promising for predicting
activities and properties of new molecules. Unfortunately, their inherent black-box character …

Abstract Machine learning (ML) systems can model quantitative structure-property
relationships (QSPR) using existing experimental data and make property predictions for …

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where
demand for new treatments greatly exceeds approved treatment options. This is complicated …

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML)
models for drug development, effectively interpreting their predictions remains a challenge …

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research
and development has to date focused on research: target identification; docking-, fragment …

Because of the growing bacterial resistance to antibiotics, the discovery of new antibiotics is
critical. The search for new antimicrobial drugs that are effective in treating new and existing …

Artificial intelligence algorithms have been increasingly applied in drug development due to
their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to …

Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-
world problems. However, like human-written software, DNNs can have bugs and can be …