David Ryan Koes
@dkoes.compstruct.org
Removing barriers to computational drug discovery one bit at a time. Associate Professor in Computational and Systems Biology at the University of Pittsburgh.
https://bits.csb.pitt.edu/
https://bits.csb.pitt.edu/
Here is how Boltz-1 (green), DynamicBind (magenta), and GNINA (blue) dock a collection of random molecules. GNINA, using a classical sampling algorithm (MCMC) hits all concave regions while the ML samplers have distinct preferences. Boltz is the most likely to induce a fit.
November 22, 2024 at 6:27 PM
Here is how Boltz-1 (green), DynamicBind (magenta), and GNINA (blue) dock a collection of random molecules. GNINA, using a classical sampling algorithm (MCMC) hits all concave regions while the ML samplers have distinct preferences. Boltz is the most likely to induce a fit.
Some fun with Boltz-1 (www.biorxiv.org/content/10.1...). I generated 1000 samples profilin (green) and compare them to the NMR structures (cyan). Samples were generated by docking 1000 random ligands. NMR structures show more conformational diversity.
November 21, 2024 at 9:40 PM
Some fun with Boltz-1 (www.biorxiv.org/content/10.1...). I generated 1000 samples profilin (green) and compare them to the NMR structures (cyan). Samples were generated by docking 1000 random ligands. NMR structures show more conformational diversity.