Xiao-Chen Bo

CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.

It is suggested that incorporating bioactivity similarity during pretraining can enrich molecular representations and improve drug-drug interaction prediction and exhibit enhanced sensitivity in distinguishing structurally similar molecules with subtle chemical differences.

DILI Prediction Using Molecular Fingerprints and ChemBERTa

Drug-induced liver injury (DILI) is a major cause of drug development failure and post-marketing withdrawal. Accurate computational prediction of hepatotoxicity is hindered by complex biological mechanisms and scarce labeled toxicity data. Although pretrained molecular language models like ChemBERTa perform well in mol...

PR-GVAE: Property-Controllable Molecular Generation via Conditional Graph Variational Autoencoder

Molecular generation is a core task in drug discovery. Although existing deep generative models can produce valid molecular structures, they lack precise control over molecular properties such as lipophilicity (logP) and drug-likeness (QED). This paper proposes Property-Regularized Graph Variational Autoencoder (PR-GVA...

SynerMolNet: Molecular Representation and Attention Fusion for Cancer Drug Synergy Prediction

AI-Driven Drug-Target Interaction Prediction: From Data Representation to Model Design.

A systematic reference for future algorithm design, mechanism exploration, and real-world drug discovery applications for AI-driven DTI prediction methodologies, covering binding theories, task formulations, data representation, model design, translational applications, and unresolved challenges.

A Drug-Target Affinity Prediction Model Based on Bayesian Meta-Learning and Uncertainty Fusion

In the early stages of drug discovery, predicting drug-target affinity is a crucial task. Due to the vast scale of genomic and chemical spaces, traditional biological methods are time-consuming, labor-intensive, and resource-demanding. As a result, machine learning-based computational methods have emerged to narrow dow...