no code implementations • 15 Dec 2023 • Mahdi Ghorbani, Leo Gendelev, Paul Beroza, Michael J. Keiser
In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures.
no code implementations • 12 Jan 2022 • Mahdi Ghorbani, Samarjeet Prasad, Jeffery B. Klauda, Bernard R. Brooks
In this contribution, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories.
no code implementations • 24 Dec 2021 • Amir Shaikhha, Marios Kelepeshis, Mahdi Ghorbani
Furthermore, we show that the performance of the code generated by our framework either outperforms or is on par with the state-of-the-art analytical query engines and a recent in-database machine learning framework.
no code implementations • 27 Aug 2021 • Mahdi Ghorbani, Samarjeet Prasad, Jeffery B. Klauda, Bernard R. Brooks
We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding.
no code implementations • 9 Oct 2020 • Mahdi Ghorbani, Fahimeh Fooladgar, Shohreh Kasaei
The proposed method has been devoted to both lightweight image classification and encoder-decoder architectures to boost the performance of small and compact models without incurring extra computational overhead at the inference process.