
pan tom
@daspantom
ID: 2738872186
10-08-2014 09:06:07
140 Tweet
115 Followers
312 Following

Excited to share our new preprint 🥳🥳 Implicit guidance with PropEn: Match your data to follow the gradient 🔗 arxiv.org/pdf/2405.18075 Joint work with Andreas Loukas, Kyunghyun Cho, VGligorijevic at @prescientdesign & Genentech



Our small molecule team at Prescient Design within Genentech is hiring for two Machine Learning Scientists. Come work with us to impact drug discovery through computation and machine learning! roche.wd3.myworkdayjobs.com/ROG-A2O-GENE/j… roche.wd3.myworkdayjobs.com/ROG-A2O-GENE/j… Vishnu Sresht





Machine Learning enhanced optimization of Quantum Circuits strikes back ☄️ Beyond excited to present our new work on Adaptive Shot Control for VQEs using Gaussian Processes at ICML Conference 🎉 Check out the paper now openreview.net/pdf?id=dSrdnhL… #icml2024


Great collaborative work with Lorenz Richter!

We have a few spots left! Register now at indico.hiskp.uni-bonn.de/event/443/ 14 technical talks 3 keynotes 3 panel discussions 1 poster session and much more... Including speakers like (in random order) pan tom, Jonas Köhler, Michael Albergo, Lei Wang, Lorenz Richter, and many more!

JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling Prescient Design • JAMUN introduces a generative model based on Walk-Jump Sampling (WJS) to efficiently generate molecular conformational ensembles, outperforming traditional molecular



My team at Prescient Design is hiring! 🎉 Keunwoo Choi, Kyunghyun Cho, and I are looking for ML engineers to develop our internal LLMs & foundation models for drug discovery. Feel free to DM or reach out to us at #neurips2024! Link: tinyurl.com/prescient-llm




Our new work arxiv.org/pdf/2503.01006 extends the theory of diffusion bridges to degenerate noise settings, including underdamped Langevin dynamics (with Denis Blessing, Julius Berner). This enables more efficient diffusion-based sampling with substantially fewer discretization steps.




Excited to have contributed to this amazing work by Lorenz Vaitl! arxiv.org/abs/2505.10139
