ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

2 code implementations • 7 May 2024 • Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro Ramírez, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen Ahmad, Vincent Moens, Woody Sherman, Simone Sciabola, Gianni de Fabritiis

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties.

Benchmarking Decision Making +4

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks

2 code implementations • 13 May 2021 • David F. Hahn, Christopher I. Bayly, Hannah E. Bruce Macdonald, John D. Chodera, Vytautas Gapsys, Antonia S. J. S. Mey, David L. Mobley, Laura Perez Benito, Christina E. M. Schindler, Gary Tresadern, Gregory L. Warren

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs.

Benchmarking Drug Discovery

Best Practices for Alchemical Free Energy Calculations

1 code implementation • 7 Aug 2020 • Antonia S. J. S. Mey, Bryce Allen, Hannah E. Bruce Macdonald, John D. Chodera, Maximilian Kuhn, Julien Michel, David L. Mobley, Levi N. Naden, Samarjeet Prasad, Andrea Rizzi, Jenke Scheen, Michael R. Shirts, Gary Tresadern, Huafeng Xu

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another.