The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …

Highlights•Deep learning methods have gained outstanding achievements.•We review deep
learning methods/tools relevant to drug discovery research.•We discuss opportunities …

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural
networks such as deep neural networks or recurrent networks drive this area. Numerous …

Deep learning is a wildly popular topic in machine learning and is structured as a series of
nonlinear layers that learns various levels of data representations. Deep learning employs …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

How to produce expressive molecular representations is a fundamental challenge in
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …

Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …

Successful drug discovery projects require control and optimization of compound properties
related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype …

Globally, the prognosis of heart disease can be improved by early diagnosis and treatment.
However, existing automatic systems for diagnosing heart disease are hampered by the …

Angiotensin‐I‐converting enzyme (ACE) inhibitory peptides are able to inhibit the activity of
ACE, which is the key enzymatic factor mediating systemic hypertension. ACE‐inhibitory …