Shiye Tian

Hui Liu, Shiye Tian, Xuejun Liu

The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, previous methods predominantly rely on expression profiles to guide molecule generation, but overlook the perturbative effect of the molecules on cellular contexts. To overcome this limitation, we propose SmilesGEN, a novel generative model based on variational autoencoder (VAE) architecture to generate molecules with potential therapeutic effects. SmilesGEN integrates a pre-trained drug VAE (SmilesNet) with an expression profile VAE (ProfileNet), jointly modeling the interplay between drug perturbations and transcriptional responses in a common latent space. Specifically, ProfileNet is imposed to reconstruct pre-treatment expression profiles when eliminating drug-induced perturbations in the latent space, while SmilesNet is informed by desired expression profiles to generate drug-like molecules. Our empirical experiments demonstrate that SmilesGEN outperforms current state-of-the-art models in generating molecules with higher degree of validity, uniqueness, novelty, as well as higher Tanimoto similarity to known ligands targeting the relevant proteins. Moreover, we evaluate SmilesGEN for scaffold-based molecule optimization and generation of therapeutic agents, and confirmed its superior performance in generating molecules with higher similarity to approved drugs. SmilesGEN establishes a robust framework that leverages gene signatures to generate drug-like molecules that hold promising potential to induce desirable cellular phenotypic changes.

Xing Fang, Chenpeng Yu, Shiye Tian et al.

The anti-cancer immune response relies on the bindings between T-cell receptors (TCRs) and antigens, which elicits adaptive immunity to eliminate tumor cells. This ability of the immune system to respond to novel various neoantigens arises from the immense diversity of TCR repository. However, TCR diversity poses a significant challenge on accurately predicting antigen-TCR bindings. In this study, we introduce a lightweight masked language model, termed tcrLM, to address this challenge. Our approach involves randomly masking segments of TCR sequences and training tcrLM to infer the masked segments, thereby enabling the extraction of expressive features from TCR sequences. To further enhance robustness, we incorporate virtual adversarial training into tcrLM. We construct the largest TCR CDR3 sequence set with more than 100 million distinct sequences, and pretrain tcrLM on these sequences. The pre-trained encoder is subsequently applied to predict TCR-antigen binding specificity. We evaluate model performance on three test datasets: independent, external, and COVID-19 test set. The results demonstrate that tcrLM not only surpasses existing TCR-antigen binding prediction methods, but also outperforms other mainstream protein language models. More interestingly, tcrLM effectively captures the biochemical properties and positional preference of amino acids within TCR sequences. Additionally, the predicted TCR-neoantigen binding scores indicates the immunotherapy responses and clinical outcomes in a melanoma cohort. These findings demonstrate the potential of tcrLM in predicting TCR-antigen binding specificity, with significant implications for advancing immunotherapy and personalized medicine.