1. Recent advances in alchemical binding free energy calculations have been used to solve various drug discovery challenges.
2. These calculations can be used for fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning.
3. Machine learning models are being developed to replace costly binding free energy calculations and allow for extended chemical space exploration.
The article is generally reliable and trustworthy as it provides a comprehensive overview of recent advances in alchemical binding free energy calculations for drug discovery. The article is well-researched and provides detailed information on the various applications of these calculations as well as the potential benefits of using machine learning models to replace them. The article does not appear to be biased or one-sided in its reporting, nor does it contain any promotional content or partiality. It also does not appear to omit any points of consideration or evidence for the claims made, nor does it present any counterarguments that could challenge the validity of the claims made in the article. Furthermore, possible risks associated with using these methods are noted throughout the article. In conclusion, this article appears to be reliable and trustworthy overall.