Bio

I am a Principal Scientist at Biogen, where I lead a machine learning group focused on accelerating drug discovery and design. Our research spans deep generative models for both small molecule and antibody design, as well as reinforcement learning for multi-objective molecular and antibody optimization.

News

  • 2026 / 09 Our review “Machine Learning-Aided Small-Molecule Virtual Screening: Recent Advances and Future Perspectives” is published in WIREs Computational Molecular Science 🎉 [Link]
  • 2026 / 03 Our paper “Application of protein language models for antibody developability prediction” is accepted at mAbs 🎉 [Link]
  • 2025 / 09 Our paper “Iterative Foundation Model Fine-Tuning on Multiple Rewards” is accepted at NeurIPS 2025 🎉 [Link]
  • 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 two research fellows working on AI/ML for protein design and small molecule drug design at Biogen.
  • 2024 / 02 Our paper on pretrained active learning for virtual screening is accepted by Journal of Chemical Information and Modeling [Link]
  • 2024 / 02 I am hiring a research fellow (entry-level PhD graduate) working on AI/ML for drug design at Biogen. [Apply 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]
Earlier news
  • 2022 / 08 Our paper on deep multimodal learning for functional interpretation of genetic variants in personal genomes is accepted by Bioinformatics [Link]
  • 2022 / 04 Our paper on deep transfer learning for predicting functional variants is accepted by Bioinformatics [Link]

Publications

  1. Wang, Y.; Bansal, N.; Wa, S.; Sciabola, S.; Wang, Y. (2026) Machine Learning-Aided Small-Molecule Virtual Screening: Recent Advances and Future Perspectives. WIREs Computational Molecular Science [Link]
  2. Amini, S.; Huang, Y.; Julian, M.; Palmer, C.; Sciabola, S.; Wang, Y. (2026) Application of Protein Language Models for Antibody Developability Prediction. mAbs [Link]
  3. Ghari, P. M.; Sciabola, S.; Wang, Y. (2025) Iterative Foundation Model Fine-Tuning on Multiple Rewards. NeurIPS 2025 [Link]
  4. 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 [Link]
  5. Bansal, N.; Wang, Y.; Sciabola, S. (2024) Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations. Molecules
  6. 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 [Link]
  7. Wang, Y.; Zhao, H.; Sciabola, S.; Wang, W. (2023) cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation. Molecules [Link]
  8. Wang, Y.; Chen, L. (2022) DeepPerVar: A Multimodal Deep Learning Framework for Functional Interpretation of Genetic Variants in Personal Genome. Bioinformatics [Link]
  9. Chen, L.; Wang, Y. (2022) Exploiting Deep Transfer Learning for the Prediction of Functional Noncoding Variants Using Genomic Sequence. Bioinformatics [Link]
  10. 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
  11. 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
  12. Chen, L.; Wang, Y.; Yao, B.; Mitra, A.; Wang, X.; Qin, X. (2018) TIVAN: Tissue-Specific cis-eQTL Single Nucleotide Variant Annotation and Prediction. Bioinformatics