Education

• Looking for PhD opportunities in AI for Medicine/Drug Discovery for Fall 2025
• M.S. in School of Software, Henan University, Sep 2022 - Jun 2025
• B.S. in College of Information Engineering, Henan University of Science and Technology, Sep 2018 - Jun 2022

RESEARCH EXPERIENCE

• Enhancing generalizability and performance in drug–target interaction identification by integrating pharmacophore and pre-trained models
  • First author, Oral presentation at ISMB 2024 [Nov 2023 - Feb 2024]
  • Developed HeteroDTA, a novel drug-target affinity prediction method that integrates multi-view compound feature extraction and pre-trained models to enhance generalization and reduce dependence on labeled data.
  • Achieved significant performance improvements over existing methods on public benchmark datasets and demonstrated effectiveness in real-world drug discovery studies.
• GraphkmerDTA: Integrating Local Sequence Patterns and Topological Information for Drug-Target Binding Affinity Prediction and Its Application in Multi-target Anti-Alzheimer’s Drug Discovery
  • First author, Accepted by Molecular Diversity [Nov 2022 - May 2023]
  • Proposed a novel deep learning method, GraphkmerDTA, integrating Kmer features with structural topology to predict drug-target binding affinity (DTA), outperforming existing methods on benchmark datasets.
  • Applied GraphkmerDTA in an AI-assisted network pharmacology strategy to discover key anti-Alzheimer’s disease components from the Lonicera japonica flower, demonstrating the model’s effectiveness in multitarget drug discovery
• ComNet: A Multi-View Deep Learning Model for Predicting Drug Combination Side Effects
  • First author, Accepted by Journal of Chemical Information and Modeling [Jul 2024 - Sep 2024]
  • A novel drug combination side effect prediction model named ComNet is proposed, which improves the prediction accuracy and reliability of complex drug interactions by combining multi-modal feature extraction, multi-scale subgraph information fusion and multi-modal feature fusion mechanism.
  • Experiments on multiple shared data sets show that the model is superior to the existing model, and the practicability is demonstrated by case studies.
• Integration of AI-enhanced network pharmacology and experimental validation to reveal molecular mechanisms of multi-targets effects of Forsythia suspensa against Alzheimer’s Disease
  • Co-author, Submission [Jun 2024 - Sep 2024]
  • Integrating network pharmacology, artificial intelligence, molecular docking, and wet-lab experiments to investigate the core components, relevant targets, and disease pathways associated with Forsythia suspensa in the context of anti-Alzheimer’s disease (AD) activity.
  • Responsible for designing an AI-driven virtual screening protocol capable of predicting the affinity between individual traditional Chinese medicine compounds and thousands of AD-related targets within minutes and proposing a deep learning model named AlzNet, which combines graph neural networks with a multi-scale feature fusion mechanism and further optimizes predictive performance using attention mechanisms. Ultimately, the pre-trained AlzNet model was employed to screen and rapidly identify potential broad-spectrum anti-AD candidates. This research strategy effectively harnesses the efficiency of artificial intelligence while integrating the reliability of traditional pharmacology, significantly enhancing the efficiency and accuracy of drug development.

HONORS & AWARDS

• The First prize of the National Artificial Intelligence Application Scene Innovation Challenge Competition Dec 2024
• The First prize of the 6th National College Students’ Intelligent Technology Application Competition Oct 2024
• National Award of “Challenge Cup” National College Student Business Plan Competition 2024 July 2024
• The Second Prize of Huawei Cloud Developer Competition in Jiangsu Province Sep 2024
• The Third Prize of 4th “Duzheng Cup” Biomedical AI Innovation Application Competition June 2024
• The Third Prize of China International College Students’ Innovation Competition 2024 in Henan University June 2024
• Honorable Mention of Bio-OS Open Source Competition Dec 2023
• The Third Prize of Huawei Cloud Yellow River Kunpeng Developer Competition in Henan Province Nov 2023
• Honorable Mention of the first global AI Drug Development Algorithm Competition 2023 Sep 2023
• Patent: An enhanced drug-drug interaction prediction method based on multimodal drug features Sep 2024
• Patent: An enhanced protein-drug interaction prediction method and system based on integrated Kmer and topological features (CN202311097806.2) Sep 2023

LEADERSHIP EXPERIENCE

• 2023 SICBC-AI Algorithms for Drug Screening
  • Team leader and developer [Jan 2024 - Present]
  • I formed a team to participate in a highly challenging drug screening competition targeting GluN1/GluN3A receptors. We needed to identify hit compounds with high activity and specificity from a vast library of approximately 18 million compounds. Utilizing drug-target affinity prediction models, we successfully identified a compound with an activity of 1.98μM. Out of all submissions, the organizers will select no less than 10 molecules per team, leading to about 1,000 molecules that will undergo high-throughput screening based on fluorescence signal changes. The top 100 active molecules will then advance to the preliminary assessment phase. Our identified compound has been included in the top 100.
• Cloud-based intelligent AI drug screening platform
  • Team leader and developer [Sep 2023 - Present]
  • This project is dedicated to creating an intelligent drug screening platform using cloud computing, driven by an advanced AI algorithm for drug-target affinity prediction. The platform integrates the collection and preprocessing of small molecule drug libraries with AI-powered screening and molecular docking techniques. This approach significantly boosts the efficiency and accuracy of drug discovery while cutting costs and development time.

SKILLS

• Proficient in programming languages such as Python, Java, and C/C++.
• Skilled in scientific computing and cheminformatics tools including Numpy, Pandas, RDKit, and various graphical and visualization tools.
• Experienced in working within Linux environments and proficient in the PyTorch deep learning framework.
• Specialized in developing models using Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and other advanced techniques.
• Adept at developing websites and applications, with expertise in development frameworks and languages such as Unimap, Springboot, Spring, Java, JavaScript, and Vue.

SUMMARY

• Cross background in combining bioinformatics, computational biology and other fields with AI
• Engaged in the development of biopharmaceutical-related AI algorithms (e.g. interaction prediction)
• Effectively plan and manage projects while collaborating with experts from other fields to ensure the successful translation of research outcomes into practical applications.
• Passionate about scientific research, strong communication and teamwork skills, and strong interest in AI and new drug development.