Machine Learning-Aided Small-Molecule Virtual Screening: Recent Advances and Future Perspectives
We comprehensively review how AI and machine learning are reshaping small-molecule virtual screening, spanning AI-driven scoring functions, efficient search algorithms, and the paradigm shift toward generative de novo molecular design.
Sep 1, 2026
Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
We introduce Elite-Weighted Supervised Fine-tuning (EW-SFT), which passes reward through elite selection and updates the model with its own pretraining loss, yielding a single optimizer that transfers across autoregressive, masked-diffusion, and discrete-flow molecular generators.
Aug 31, 2026
Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance
We propose pretrained embedding distance (PED), a molecular similarity measure computed directly from pretrained models without task-specific training, effective for both ranking molecules in virtual screening and guiding molecular generation via reward design.
Apr 27, 2026
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning
We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.
Jul 23, 2025
🎉 I joined Biogen!
I’m happy to share that I’m starting a new position as Machine Learning Research Fellow at Biogen!
Feb 17, 2025
Motif-based graph representation learning with application to chemical molecules
We designed a novel convolution module for graph representational learning on molecules with an efficient pretraining strategy, enabling the capture of local structural and semantic information from graph motifs.
Jan 11, 2023