Update README.md
Browse files
README.md
CHANGED
|
@@ -11,18 +11,15 @@ model-index:
|
|
| 11 |
- name: Accuracy
|
| 12 |
type: Accuracy
|
| 13 |
value: 0.6199
|
| 14 |
-
- name:
|
| 15 |
-
type: F1
|
| 16 |
-
value: 0.6127
|
| 17 |
-
- name: Weighted F1
|
| 18 |
-
type: F1
|
| 19 |
-
value: 0.6127
|
| 20 |
-
- name: Macro Precision
|
| 21 |
type: Precision
|
| 22 |
value: 0.6142
|
| 23 |
-
- name:
|
| 24 |
type: Recall
|
| 25 |
value: 0.6199
|
|
|
|
|
|
|
|
|
|
| 26 |
license: cc-by-nc-sa-4.0
|
| 27 |
metrics:
|
| 28 |
- accuracy
|
|
@@ -40,6 +37,9 @@ tags:
|
|
| 40 |
|
| 41 |
# ChemFIE-DTP (DrugTargetPrediction - 221 Classes)
|
| 42 |
|
|
|
|
|
|
|
|
|
|
| 43 |
This model is a multiclass sequence classification for 221 human protein drug targets, based on [gbyuvd/chemselfies-base-bertmlm](https://huggingface.co/gbyuvd/chemselfies-base-bertmlm) fine-tuned on a dataset derived from ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded Strings). The model was trained on a selected and balanced dataset of around 154k examples covering 221 distinct human protein targets. Data selection criteria included specific activity types (IC50, Ki, EC50) with values ≤ 10 µM, assay confidence scores ≥ 7, and exact activity relations. Among all drug target classes found in ChemBL34, classes with at least 1000 examples are selected then capped at 1000 for those with more samples. Building upon the pre-trained base model's pre-existing knowledge of SELFIES, this model is originally intended to validate the capabilities of the light-weight base model to be fine-tuned for various tasks, and for this model case, it might be useful for tasks related to early-stage drug discovery and target prediction (e.g. compounds annotations) - though its performance and applicability should be carefully evaluated for specific use cases (see [Evaluation](#evaluation))
|
| 44 |
|
| 45 |
- List of classes available in the "label_dict.json"
|
|
|
|
| 11 |
- name: Accuracy
|
| 12 |
type: Accuracy
|
| 13 |
value: 0.6199
|
| 14 |
+
- name: Weighted Precision
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 15 |
type: Precision
|
| 16 |
value: 0.6142
|
| 17 |
+
- name: Weighted Recall
|
| 18 |
type: Recall
|
| 19 |
value: 0.6199
|
| 20 |
+
- name: Weighted F1
|
| 21 |
+
type: F1
|
| 22 |
+
value: 0.6127
|
| 23 |
license: cc-by-nc-sa-4.0
|
| 24 |
metrics:
|
| 25 |
- accuracy
|
|
|
|
| 37 |
|
| 38 |
# ChemFIE-DTP (DrugTargetPrediction - 221 Classes)
|
| 39 |
|
| 40 |
+
|
| 41 |
+
[](https://ko-fi.com/O4O710GFBZ)
|
| 42 |
+
|
| 43 |
This model is a multiclass sequence classification for 221 human protein drug targets, based on [gbyuvd/chemselfies-base-bertmlm](https://huggingface.co/gbyuvd/chemselfies-base-bertmlm) fine-tuned on a dataset derived from ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded Strings). The model was trained on a selected and balanced dataset of around 154k examples covering 221 distinct human protein targets. Data selection criteria included specific activity types (IC50, Ki, EC50) with values ≤ 10 µM, assay confidence scores ≥ 7, and exact activity relations. Among all drug target classes found in ChemBL34, classes with at least 1000 examples are selected then capped at 1000 for those with more samples. Building upon the pre-trained base model's pre-existing knowledge of SELFIES, this model is originally intended to validate the capabilities of the light-weight base model to be fine-tuned for various tasks, and for this model case, it might be useful for tasks related to early-stage drug discovery and target prediction (e.g. compounds annotations) - though its performance and applicability should be carefully evaluated for specific use cases (see [Evaluation](#evaluation))
|
| 44 |
|
| 45 |
- List of classes available in the "label_dict.json"
|