# TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks

arXiv: https://arxiv.org/abs/2105.03323 · HTML: https://mlsanity.com/paper/2105.03323/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules](https://mlsanity.com/paper/1703.07076/) (2017)
- ["Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models](https://mlsanity.com/paper/1711.04810/) (2017)
- [SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties](https://mlsanity.com/paper/1712.02034/) (2017)
- [Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders](https://mlsanity.com/paper/1904.11223/) (2019)

## Built on by (most cited first, 16 of 16)

- [Tx-LLM: A Large Language Model for Therapeutics](https://mlsanity.com/paper/2406.06316/) (2024)
- [MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language](https://mlsanity.com/paper/2410.22367/) (2024)
- [Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding](https://mlsanity.com/paper/2507.03197/) (2025)
- [Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity](https://mlsanity.com/paper/2206.11255/) (2022)
- [Contrastive learning of T cell receptor representations](https://mlsanity.com/paper/2406.06397/) (2024)
- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)
- [A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design](https://mlsanity.com/paper/2508.10899/) (2025)
- [It's FLAN time! Summing feature-wise latent representations for interpretability](https://mlsanity.com/paper/2106.10086/) (2021)
- [T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy](https://mlsanity.com/paper/2303.02162/) (2023)
- [MATE-Pred: Multimodal Attention-based TCR-Epitope interaction Predictor](https://mlsanity.com/paper/2401.08619/) (2023)
- [A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules](https://mlsanity.com/paper/2405.06653/) (2024)
- [tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificity](https://mlsanity.com/paper/2406.16995/) (2024)
- [Computational design of target-specific linear peptide binders with TransformerBeta](https://mlsanity.com/paper/2410.16302/) (2024)
- [DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction](https://mlsanity.com/paper/2411.17798/) (2024)
- [Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations](https://mlsanity.com/paper/2505.01433/) (2025)
- [PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning](https://mlsanity.com/paper/2610.04850/) (2026)

## Abstract (arXiv)

Motivation: The activity of the adaptive immune system is governed by T-cells and their specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent advances in experimental techniques have enabled sequencing of TCRs and their antigenic targets (epitopes), allowing to research the missing link between TCR sequence and epitope binding specificity. Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance. Here, we establish a k-nearest-neighbor (K-NN) classifier as a strong baseline and then propose TITAN (Tcr epITope bimodal Attention Networks), a bimodal neural network that explicitly encodes both TCR sequences and epitopes to enable the independent study of generalization capabilities to unseen TCRs and/or epitopes. Results: By encoding epitopes at the atomic level with SMILES sequences, we leverage transfer learning and data augmentation to enrich the input data space and boost performance. TITAN achieves high performance in the prediction of specificity of unseen TCRs (ROC-AUC 0.87 in 10-fold CV) and surpasses the results of the current state-of-the-art (ImRex) by a large margin. Notably, our Levenshtein-distance-based K-NN classifier also exhibits competitive performance on unseen TCRs. While the generalization to unseen epitopes remains challenging, we report two major breakthroughs. First, by dissecting the attention heatmaps, we demonstrate that the sparsity of available epitope data favors an implicit treatment of epitopes as classes. This may be a general problem that limits unseen epitope performance for sufficiently complex models. Second, we show that TITAN nevertheless exhibits significantly improved performance on unseen epitopes and is capable of focusing attention on chemically meaningful molecular structures.