Update README.md

README.md

@@ -41,7 +41,7 @@ tags:
 
 # ChemFIE-DTP (DrugTargetPrediction - 221 Classes)
 
-This model is a BERT-like sequence classification model 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))
+This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from [gbyuvd/chemselfies-base-bertmlm](https://huggingface.co/gbyuvd/chemselfies-base-bertmlm) on a dataset derived 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))
 
 - List of classes available in the "label_dict.json"
 - Its performance on each classes available in "test_result.txt"
@@ -220,7 +220,7 @@ Bioactive compounds from [ChemBL34](https://ftp.ebi.ac.uk/pub/databases/chembl/C
 Dataset Details:
 - Total training examples: 154,700
 - Number of classes: 221 distinct human protein drug targets
-- Organism: Homo sapiens
+- Organism: _Homo sapiens_
 - Number of train examples for each class: 700
 - Number of validation examples for each class: 100
 - Number of held out test examples for each class: 200
@@ -303,6 +303,8 @@ Both macro (unweighted mean of all classes) and weighted (weighted by class supp
 ### Results
 
 #### General
+- Baseline with random guess (1/221): 0.0045
+
 - Accuracy: 0.6199
 - Macro F1: 0.6127
 - Weighted F1: 0.6127