Before revision:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
After revision:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically . Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.