Improving Antibody Humanness Prediction using Patent Data
Authors: Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate that the learned model consistently outperforms the alternative baselines and establishes new state-of-the-art on five out of six inference tasks, irrespective of the used metric. |
| Researcher Affiliation | Collaboration | 1Centre for AI, Bio Pharmaceuticals R&D, Astra Zeneca 2Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge 3Biologics Engineering, Oncology R&D, Astra Zeneca. |
| Pseudocode | No | The paper describes the Self PAD framework and its training processes using explanatory text and diagrams (Figure 1), but it does not include formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | The code for Self PAD is available at: https://github.com/AstraZeneca/SelfPAD |
| Open Datasets | Yes | We use patented antibody database (PAD) (Krawczyk et al., 2021)... 553 Therapeutics is a dataset from Prihoda et al. (2022)... 217 immunogenicity refers to the dataset obtained from Prihoda et al. (2022)... 25 humanization data refers to the dataset... Marks et al. (2021). |
| Dataset Splits | Yes | The training set is then split into two folds, 90% for training and 10% for validation, and the model is fine-tuned with cross-entropy loss for 25 epochs. |
| Hardware Specification | Yes | We used a compute cluster consisting of A10G GPUs throughout this work. |
| Software Dependencies | Yes | We implemented our work using Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | We pre-trained the model with a batch size of 100 for 1000 epochs (see Figure 5 in the appendix)... we used cross-entropy loss with label smoothing (configured to 0.5) and a batch size of 512 for 25 epochs... Table 9 lists hyperparameters used for pre-training and fine-tuning stages. |