Drugdesign

Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules

T Li, H Liu, H Guo, M Gerstein, MR Min (2026). Nat Commun 17.

A discard-and-restart MD algorithm for the sampling of protein intermediate states

A Ianeselli, J Howard, MB Gerstein (2025). Biophys J 124: 3895-3907.

Network-based drug repurposing for psychiatric disorders using single-cell genomics

C Gupta, NC Kalafut, D Clarke, JJ Choi, KH Arachchilage, S Khullar, Y Xia, X Zhou, C Dursun, M Gerstein, D Wang (2025). Cell Genom 5: 101003.

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems

Y Li, Z Xia, L Huang, X Wei, S Harshe, H Yang, E Luo, Z Wang, J Zhang, C Liu, B Shao, M Gerstein (2025). ICLR.

Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling

HJ Lee, PS Emani, MB Gerstein (2024). J Chem Inf Model 64: 8684-8704.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

X Tang, H Dai, E Knight, F Wu, Y Li, T Li, M Gerstein (2024). Brief Bioinform 25.

Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering

T Li, H Guo, F Grazioli, MB Gerstein, MR Min (2023). NeurIPS 2023.

Predicting the frequencies of drug side effects.

D Galeano, S Li, M Gerstein, A Paccanaro (2020). Nat Commun 11: 4575.

Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures.

S Kumar, D Clarke, MB Gerstein (2019). Proc Natl Acad Sci U S A 116: 18962-18970.

Localized structural frustration for evaluating the impact of sequence variants.

S Kumar, D Clarke, M Gerstein (2016). Nucleic Acids Res 44: 10062-10073.

High-order neural networks and kernel methods for peptide-MHC binding prediction.

PP Kuksa, MR Min, R Dugar, M Gerstein (2015). Bioinformatics 31: 3600-7.