Chemprop: A Machine Learning Package for Chemical Property Prediction #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

Excited to share that our Chemprop package paper is published on JCIM pubs.acs.org/doi/10.1021/ac… #machinelearning #ML

Chemprop: A Machine Learning Package for Chemical Property Prediction #DeepLearning pubs.acs.org/doi/10.1021/ac… Esther Heid Kevin Greenman Yunsie Chung Florence Vermeire Chas McGill #JCIM Vol64 Issue1 #ApplicationNote

Meet Professor Kevin Greenman: Final-year Ph.D. candidate in chemical engineering and computation at MIT, NSF graduate research fellow. Watch the clip to learn more! #ChemicalEngineering #CatholicScientists #MIT #Innovation

Congrats to Dr. Kevin Greenman on defending his Ph.D. thesis at MIT! His groundbreaking work in chemical engineering uses AI to optimize experiments, aiding novel molecule discovery for displays and biomedical imaging. He continues his research CatholicTech 🚀📚

🎉 We're thrilled to announce our 1st class of MolSSI Faculty Fellows, who will collaborate with us over the next 2 years to create cutting-edge curricular resources, integrating programming, data, & computational education into their courses. Welcome! 👏 molssi.org/fellowship/202…

What does the development of artificial intelligence say about the human soul? What leads scientific-minded people to convert to Christianity? The 2024 convention of the Society of Catholic Scientists will tackle both these questions and many more. catholicnewsagency.com/news/257864/on…

We are delighted to host our first class of ACT-CMS (Accelerating Curricular Transformation in the Computational Molecular Sciences) Faculty Fellows for their workshop this week! They are developing learning activities to integrate programming & computation into their courses.😀

Zero-shot extrapolation for out-of-distribution (OOD) chemical property prediction is an important step towards high-performance materials discovery. Check out our spotlight at the #NeurIPS AI for Accelerated Materials Design Workshop! openreview.net/pdf?id=HkfnueE…

📢New preprint out! We constrain the molecular generation space to follow the "symmetry" of patented molecules that are likely to be synthesizable. Achieved with "symmetry-aware" fragment decomposition, and a constrained Monte Carlo Tree Search generator. arxiv.org/abs/2410.08833

We compared the calibration of various machine learning uncertainty estimation methods for protein engineering. No method excels across all scenarios, and uncertainty-based strategies for optimization often did not outperform methods without uncertainty.

Benchmarking uncertainty quantification for protein engineering PLOS Comp Biol 1. Machine learning (ML) models have accelerated protein engineering, but estimating model uncertainty remains critical for optimizing biological properties and guiding experimental designs

Out now in Chemical Science! Give it a read if you're interested in molecular design, synthesizability, and/or organic electronics! Co-authors: James Damewood, Juno Nam , Kevin Greenman , Avni Singhal , and RGB Lab @ MIT. pubs.rsc.org/en/Content/Art…

Looking forward to connecting with people at #ACSFall2025! I'll be giving a talk on Wednesday morning: "AI-driven chemistry research and undergraduate skill development with Chemprop" Session: Harnessing the Power of AI in Undergraduate Research and Training Day/Time: Wednesday