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README.md

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+# Protein_Sequence_Toxicity_Predictor
+
+## Overview
+
+The **Protein_Sequence_Toxicity_Predictor** is a specialized sequence classification model designed to predict the potential toxicity level of an arbitrary peptide or protein sequence. This model is critical for early-stage drug discovery, synthetic biology, and material science, where quick assessment of a novel sequence's risk profile is essential.
+
+It treats the amino acid sequence (e.g., "M-A-S-K...") as a sentence and the amino acids as tokens, adapting the BERT architecture for biological sequence understanding.
+
+## Model Architecture
+
+The model is based on a modified **BERT (Bidirectional Encoder Representations from Transformers)** architecture, specifically **BertForSequenceClassification**.
+
+* **Base Model:** A custom BERT-like model was trained from scratch on a large corpus of protein sequences (Uniref/Swiss-Prot), with a vocabulary size of 30, including the 20 standard amino acids, special tokens (CLS, SEP, PAD), and modified amino acids.
+* **Input:** The input is a raw amino acid sequence (e.g., 'MVLSPADKTN...').
+* **Tokenizer:** A simple BPE-like or character-level tokenizer is used, where each amino acid is treated as a single token.
+* **Classification:** A standard linear classification layer outputs the probability distribution across three toxicity classes: `Non_Toxic`, `Mildly_Toxic`, and `Highly_Toxic`.
+* **Training:** Fine-tuned on a curated dataset of experimentally verified toxic and non-toxic peptide sequences, including antimicrobial peptides and known allergens.
+
+## Intended Use
+
+* **Drug Design Safety:** Rapidly screening candidate therapeutic peptides for potential adverse effects before expensive *in vitro* or *in vivo* testing.
+* **Synthetic Biology:** Predicting the safety of novel proteins designed for industrial or research applications.
+* **Allergen Prediction:** Initial assessment of newly discovered or engineered food proteins for potential allergenic risk.
+* **High-Throughput Screening:** Integrating into automated pipelines for filtering large libraries of generated protein sequences.
+
+## Limitations
+
+* **Context Dependency:** Toxicity is often highly dependent on the protein's 3D folding, concentration, and the organism/cell line exposed. This model, being based solely on the primary (1D) sequence, cannot fully account for all these factors.
+* **Novel Sequence Space:** Performance on entirely novel sequences, especially those with non-canonical or heavily modified amino acids, may be lower than on sequences closely related to the training data.
+* **Threshold Bias:** The definition and threshold between `Mildly_Toxic` and `Highly_Toxic` are based on the training dataset's experimental methodology, which may not generalize perfectly to all external assays.
+* **Sequence Length:** The current configuration is optimized for typical peptide lengths ($\le 510$ tokens, corresponding to roughly 510 amino acids). Very large protein sequences (e.g., multi-domain proteins) will require truncation, potentially missing important structural information.