Peer-reviewed research on AI for drug discovery, ADMET prediction, and bioinformatics.
This work presents a fused deep learning framework for drug repositioning. By integrating heterogeneous biomedical signals within a unified predictive architecture, the study improves prioritization of repurposing…
Structure-based molecular docking, a cornerstone of computational drug design, is undergoing a paradigm shift fueled by deep learning (DL) innovations. However, the rapid proliferation of DL-driven docking methods…
Hunting for candidate compounds with favorable pharmacological, toxicological, and pharmacokinetic properties in drug discovery is essentially a low-data problem, as data acquisition is both challenging and expensive.…
Drug interactions pose significant challenges in clinical practice, potentially leading to adverse drug reactions, reduced efficacy, and even life-threatening consequences. DDInter 2.0 substantially expands data…
ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry…
High-throughput screening rapidly tests extensive arrays of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit-to-lead progression…
ISTransbase is an online knowledgebase for drug transporters that systematically collects inhibitor and substrate data from the literature. It supports transporter-centered exploration of drug transport mechanisms and…
Lead optimization is a crucial step in drug discovery, aiming to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to achieve a balance between…
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only on good efficacy but also on acceptable ADMET properties. ChemMORT is an automatic ADMET optimization…
This paper develops an ontology modeling framework for threat awareness in space cyber operations. The resulting ontology supports structured representation, reasoning, and analysis of satellite cyberspace threat…
Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches rely on expert knowledge and struggle with diverse…
This work studies satellite cyberspace situational understanding through ontology-guided knowledge reasoning. It builds reasoning rules and a domain knowledge base to support automatic correlation analysis of…
ABC-Net proposes a divide-and-conquer architecture for translating molecular images into SMILES strings. By decomposing the recognition problem into coordinated sub-tasks, the model improves robustness across diverse…
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. We present DDInter, a curated DDI database with comprehensive data, practical…
This work explores multitask molecular property prediction with a BERT framework enhanced by SMILES enumeration. The study shows that large-scale pretraining and sequence augmentation can improve robustness and…
This paper presents a transfer learning framework for extracting microbe-disease interactions from biomedical literature. The model improves relation mining performance under limited labeled data and supports automated…
Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that ADMET should be evaluated as early as possible. Here,…
MG-BERT introduces a BERT-style framework for unsupervised atomic representation learning from molecular structures. The learned representations improve downstream molecular property prediction and demonstrate the value…