Bio

I am a research scientist at Biogen. I received my Ph.D. in Computer Science and Software Engineering from Auburn University under Dr. Li Chen and Dr. Xiao Qin. I was a research associate at Indiana University School of Medicine.

My work at Biogen mainly foucsed on deep generative models for small molecule / antibody design, reinforcement learning for retrosynthetic planning, and deep learning for functional genomics.

News

  • [2024/08] Our paper on reinforcement learning for molecular generation is accepted by Journal of Chemical Information and Modeling [Link].
  • [2024/07] I am hiring another two research fellows working on AI/ML for protein design (Apply link) and small molecule drug design (Apply link) at Biogen.
  • [2024/02] I am hiring a research fellow (entry-level PhD graduate) working on AI/ML for drug design at Biogen. [Apply link].
  • [2024/02] Our paper on pretrained active learning for virtual screening is accepted by Journal of Chemical Information and Modeling [Link].
  • [2023/05] Our paper on ML for ADME prediction is accepted by Journal of Chemical Information and Modeling [Link].
  • [2023/04] Our cMolGPT paper is accepted by Molecules [Link].
  • [2022/08] Our paper on deep multimodal learning for functional interpretation of genetic variants in personal genome is accepted by Bioinformatics [Link].
  • [2022/04] Our paper on deep transfer learning for predicting functional variants is accepted by Bioinformatics [Link].

    • Publication

    1. Cao, Z.; Sciabola, S.; Wang, Y. (2024) Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening Journal of Chemical Information and Modeling
    2. Bansal, N.; Wang, Y.; Sciabola, S. (2024) Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations. Molecules
    3. Fang C., Wang Y. Grater R., Kapadnis S., Black C., Trapa P., Sciabola S. (2023). Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective Journal of Chemical Information and Modeling
    4. Wang Y.; Zhao, H.; Sciabola, S.; Wang, W. (2023). cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation. Molecules
    5. Wang Y., Chen L. (2022). DeepPerVar: a multimodal deep learning framework for functional interpretation of genetic variants in personal genome Bioinformatics
    6. Chen L., Wang Y. (2022). Exploiting deep transfer learning for the prediction of functional noncoding variants using genomic sequence Bioinformatics
    7. Wang Y. , Jiang Y., Yao B., Huang K., Liu Y.,Qin X., Chen L. (2021) WEVar: a novel statistical learning framework for predicting noncoding regulatory variants. Briefings in Bioinformatics
    8. Wang Y., Bhattacharya T. , Jiang Y., Qin X., Chen L. (2020) A novel deep learning method for predictive modeling of microbiome data. Briefings in Bioinformatics
    9. Chen L.,Wang Y., Yao B., Mitra A., Wang X., Qin X. (2018). TIVAN: Tissue-specific cis-eQTLsingle nucleotide variant annotation and prediction. Bioinformatics