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 should aim to address wider issues within drug design chemistry and be written in a way that is accessible to non-specialists.
Editors:
Ho Leung Ng (Associate Professor, Kansas State University) and Duc Nguyen (Assistant Professor, University of Kentucky)
Topics
Virtual Screening And Docking
QSAR
Quantum Chemistry Calculations
Molecular Dynamics Simulations
Generative Models For Molecules
Predicting Reactivity And Synthetic Routes
Physical Mechanisms For Pharmacokinetics/Drug Metabolism
Toxicology And Safety
Pharmaceutical Chemistry And Drug Formulation
Theoretical Studies Of Machine Learning Relevant To Drug Chemistry
Software Tools And Web Servers
Hardware Acceleration And Scaling In Computational Drug Design
Protein Engineering
Conformational Sampling
Free Energy Calculations
Modeling Solvation