flowdock - 0.0.3

What's Changed

• Add Docker Image by @colbyford in https://github.com/BioinfoMachineLearning/FlowDock/pull/4.
• Add revisions for 0.0.3 by @amorehead in https://github.com/BioinfoMachineLearning/FlowDock/pull/9.

Revisions for version 0.0.3: 1. Added CASP16 structure prediction benchmarking results and analysis notebook. 2. Identified a version 0.0.2 bug in the PoseBusters pip package that allowed predictions for only 14 of DockGen-E's 122 protein-ligand complexes to be scored. Addressed by accordingly renormalizing each method's average number of correct DockGen-E predictions across all three of its repeat runs. 3. Updated FlowDock's overview figure to better illustrate the network's information flow (no pun intended). 4. Added results and analysis notebook for a PoseBusters Benchmark subset case study investigating how well FlowDock can predict PoseBusters Benchmark complexes that contain multiple distinct (Tanimoto similarity < 0.6) ligands binding to the same protein chain. 5. Added support for fine-tuning FlowDock's pretrained weights using new datasets such as PLINDER! Preliminary results suggest that (minimally) doing so with PLINDER increases FlowDock's average number of correct DockGen-E predictions by a few percentage points. Neat! 6. Added results for FlowDock when providing it with Chai-1's predicted (holo-like, ligand-bound) protein structures for the PoseBusters Benchmark dataset. Surprisingly, FlowDock's performance in this setting is nearly identical to its performance when providing it with ESMFold's (apo-like, unbound) predicted protein structures. 7. Corresponding updates to the arXiv preprint should be live in early April.

New Contributors

• @colbyford made their first contribution in https://github.com/BioinfoMachineLearning/FlowDock/pull/4.

Full Changelog: https://github.com/BioinfoMachineLearning/FlowDock/compare/0.0.2...0.0.3

- Python
Published by amorehead 11 months ago

flowdock - 0.0.2

Additions: 1. Results for new baselines including AutoDock Vina + P2Rank, RoseTTAFold-All-Atom, and AlphaFold 3 (single-sequence). 2. New ablations verifying the importance of performing joint training of protein-ligand structure and binding affinity prediction (FlowDock-AFT) and FlowDock's robustness to changes in the source prediction method of its input protein structures (FlowDock-ESMFold), with the latter ablation confirming that FlowDock's docking performance can be largely maintained using the default ESMFold predicted protein input structures (compared to using AlphaFold 3's predicted protein input structures). Hurray! 3. Accordingly, an updated version of the arXiv preprint will be live by 01/18/2025.

Revisions: 1. Enhanced the DockGen result plots' formatting.

Full Changelog: https://github.com/BioinfoMachineLearning/FlowDock/compare/0.0.1...0.0.2

- Python
Published by amorehead about 1 year ago

flowdock - 0.0.1

Full Changelog: https://github.com/BioinfoMachineLearning/FlowDock/commits/0.0.1

- Python
Published by amorehead about 1 year ago