Call for Papers: AI-driven chemistry for drug design

“AI-driven drug design is one of the most exciting and dynamic fields in chemistry. We are just beginning to see what may be accomplished in the near future” – Special Issue Editor Ho Leung Ng.


AI-driven chemistry for drug design

Artificial intelligence/machine learning methods are among the most exciting research topics in drug design chemistry. This is a rapidly evolving area of research, and in a very short period of time such methods have made a great impact in multiple fields of physical chemistry, ranging from quantitative predictions of physical properties, quantum chemistry, and sampling of chemical space.

In this special issue, we seek submissions that describe novel research in applications of AI to drug discovery physical chemistry. Potential topics include, but are not limited to virtual screening and docking, structure activity relationships, quantum chemistry, molecular dynamics simulations, generative molecular models, predicting reactivity and synthetic routes, pharmacokinetics, toxicology, pharmaceutical chemistry, theoretical chemistry and computational/mathematical foundations, software tools and web servers, hardware acceleration and scaling, protein engineering, and conformational sampling.

Submissions to the Special Issue, led by Ho Leung Ng (Associate Professor, Kansas State University) and Duc Nguyen (Assistant Professor, University of Kentucky) should aim to address wider issues within drug design chemistry and be written in a way that is accessible to non-specialists.

For more information and to submit your abstract, please visit: https://peerj.com/special-issues/96-AI-drug-design

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