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| # MutDTA [](https://www.python.org/downloads/release/python-31011/)  | ||
| Improving the precision oncology pipeline by providing binding affinity purtubations predictions on a pirori identified cancer driver genes. | ||
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| # Current Progress | ||
| - [ ] Data preprocessing | ||
| - [x] PDBbind - simple enough to use. Protein seq were downloaded from UniProt. | ||
| - [x] PLATINUM - Working on getting mutated sequence data (done with mutalyzer) | ||
| - [ ] KIBA and Davis - Kinase proteins are not super relevant but could be useful for pretraining since datasets are limited. | ||
| - [ ] GENIE - This will require some physical docking methods since we have no binding affinity data for this. | ||
| - [x] Docking baseline | ||
| - [x] Set up Docking on cluster | ||
| - [x] Build scripts to automate ligand and protein prep (including grid for binding site). | ||
| - [x] Run docking on PDBbind dataset | ||
| - [ ] Model baseline | ||
| - [ ] DGraphDTA | ||
| - [x] Evaluate pretrained model on PDBbind dataset | ||
| - [x] Train model on *refined-set* PDBbind dataset and evaluate | ||
| - [ ] Train model on *general* PDBbind dataset and evaluate | ||
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| # AutoDock Vina Procedure | ||
| See [README/VINA_PROCEDURE.md](./docs/VINA_PROCEDURE.MD) for detailed steps | ||
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| ## Contribution | ||
| Try to follow [conventional commits](https://gist.github.com/Zekfad/f51cb06ac76e2457f11c80ed705c95a3). | ||
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| ### Quick examples | ||
| * `feat: new feature` | ||
| * `fix(scope): bug in scope` | ||
| * `feat!: breaking change` / `feat(scope)!: rework API` | ||
| * `chore(deps): update dependencies` | ||
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| ### Commit types | ||
| * `build`: Changes that affect the build system or external dependencies (example scopes: gulp, broccoli, npm) | ||
| * `ci`: Changes to CI configuration files and scripts (example scopes: Travis, Circle, BrowserStack, SauceLabs) | ||
| * **`chore`: Changes which doesn't change source code or tests e.g. changes to the build process, auxiliary tools, libraries** | ||
| * `docs`: Documentation only changes | ||
| * **`feat`: A new feature** | ||
| * **`fix`: A bug fix** | ||
| * `perf`: A code change that improves performance | ||
| * `refactor`: A code change that neither fixes a bug nor adds a feature | ||
| * `revert`: Revert something | ||
| * `style`: Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc) | ||
| * `test`: Adding missing tests or correcting existing tests | ||
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| ### Reminders | ||
| * Put newline before extended commit body | ||
| * More details at **[conventionalcommits.org](https://www.conventionalcommits.org/)** | ||
| # H4H Directory structure and where to find things: | ||
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| ## H4H: | ||
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| Everything important (e.g.: model weights) should be accessible through the shared project directory. | ||
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| I primarily used my home directory to store the code since I could sync it up with GitHub whereas on the shared project directory we have no internet access to perform “git pull/push” operations. | ||
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| Thus for files/folders that were too large to store in HOME, I used symbolic links to folders located in the shared project directory. However, I forget exactly how I laid everything out and I no longer have access to the VPN to connect and check. | ||
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| Nonetheless, all the important stuff we would need, like model checkpoints, should be stored in the shared project directory. | ||
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| ## GitHub \- [https://github.com/jyaacoub/MutDTA/tree/main](https://github.com/jyaacoub/MutDTA/tree/main) | ||
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| [Training splits](https://github.com/jyaacoub/MutDTA/tree/main/splits) can be found on the GitHub page as well as all my most recent code. | ||
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| # | ||
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| # GitHub issues | ||
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| All the issues we encountered with this project are tracked via GitHub. I list some of the more relevant issues below: | ||
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| ## Summary of model checkpoints/issues (found in [MutDTA/results/](https://github.com/jyaacoub/MutDTA/tree/main/results)): | ||
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| Basically the only ones that matter are `results/model_checkpoints` and `v103`. The rest are just some tests I did to resolve/debug issues. | ||
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| * [`results/model_checkpoints`](https://github.com/jyaacoub/MutDTA/tree/main/results) \- These are the models trained on *random splits* | ||
| * [`v103`](https://github.com/jyaacoub/MutDTA/issues/103) \- **pocket-only** representation checkpoints | ||
| * [`v113`](https://github.com/jyaacoub/MutDTA/issues/113) \- new training split where we excluded highly targeted (OncoKB) proteins from training. | ||
| * This leads to consistently worse performance across the board. | ||
| * [`v115`](https://github.com/jyaacoub/MutDTA/issues/115) \- since "aflow" (alphaflow edge weights) models had a smaller dataset (due to memory issues when running Alphaflow on AA sequences 1200+) we *artificially reduced the sizes of the training sets* for the other models so that we could have a fair comparison | ||
| * This didn't change much. | ||
| * [`v128`](https://github.com/jyaacoub/MutDTA/issues/128) \- Test to see if new splits were the issue (they were) | ||
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| ## OncoKB distribution drift issue with splits \- [Issue \#131](https://github.com/jyaacoub/MutDTA/issues/131) | ||
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| When we originally started looking into OncoKB I selected highly targeted proteins from OncoKB to be excluded from training sets. | ||
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| - This caused a big distribution drift issue and resulted in much worse performance, particularly with PDBbind. | ||
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| Stats on the distribution differences between the manually curated oncokb dataset split vs a random split can be found on the [issue page.](https://github.com/jyaacoub/MutDTA/issues/131#issuecomment-2276366754) | ||
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| - click the *details* button to see figures. | ||
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| ## Missing Amino Acids in PDBs for PDBbind \- [Issue\#102](https://github.com/jyaacoub/MutDTA/issues/102) | ||
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| This means for the pocket versions of our models we can’t readily use existing scripts to get the pocket sequence graph based on the PDBs provided. | ||
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| - It is possible to fix this, but it needs a LOT of effort since we would also need to retrain the PDBbind models that used graphs with the missing residues. | ||
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| ## Pocket representation version of our models \- [Issue\#103](https://github.com/jyaacoub/MutDTA/issues/103) | ||
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| This tracks how the pocket representation of Davis and Kiba models was built. The [pull request 135](https://github.com/jyaacoub/MutDTA/pull/135) resolves this with the results in the [CSV files](https://github.com/jyaacoub/MutDTA/pull/135/files#diff-470793793283a1e1b2c3c5055749ddb946413c66b5581a70bb502db544660642). | ||
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| ## |