About this Research Topic
Over the last few decades, diverse computational technics, such as machine learning techniques, even deep learning techniques, have been widely used in computational drug design and development. But it still has a long way to go for the anti-cancer drug development area. To expand the scope and ease of applicability of machine learning in the anti-cancer drug industry, it is highly desirable to propose new computational models or new computational algorithms, and more importantly, to make progress toward using artificial intelligence (AI). With the increases in computational power and the development of exciting new methods, computational techniques are promising to occupy a more central place in future drug discovery efforts.
This Research Topic will target computational methods and applications that are relevant for anti-cancer drug discovery and drug development. The traditional anti-cancer drug development suffers from huge costs and long turn-round time. Computational techniques were developed in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. Articles on anti-cancer drug development and drug discovery are welcomed for this Research Topic, including but not limited to target identification, compound screening, anti-cancer drug safety and efficacy test, anti-cancer drug repositioning, new indications for anti-cancer drug discovery using computational methods. We also encourage authors to make their codes and experimental data available to the public, which would make our topic more infusive and attractive.
• Computational methods in target identification, pharmacophore mapping, protein structure prediction.
• Molecular docking and structure-based virtual screening
• pharmacophore modeling • QSAR (2D, 3D, or 4D)
• In silico absorption, distribution, metabolism, and excretion (ADME)
• In silico toxicology
• Computer-aided drug repurposing
• Drug response prediction
• Pharmacokinetic simulations
Note: manuscripts based solely on in silico techniques without validation (independent cohort or biological validation in vitro or in vivo) will not be considered for review.
Keywords: Computational methods, machine learning, anti-cancer, drug design
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.