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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.

	# 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.