Mrinal Shekhar

Paper: arxiv.org/abs/2511.02128… Code: github.com/Graylab/DL4Pro… Authors: Michael F. Chungyoun, Sreevarsha Puvada, Gabriel Au, Courtney Thomas, Britnie J. Carpentier, Jeffrey J. Gray Acknowledgments: Sergey Lyskov, Sergey Ovchinnikov, Johns Hopkins students of 2023 540.614/414 Protein Structure Prediction course, and the Johns Hopkins Center for Teaching Excellence and Innovation - Instructional Enhancement Grant.

I received many requests to share materials from our undergraduate course “Machine Learning in Chemistry” — here you go! A preprint summarizing insights and lessons learned: chemrxiv.org/engage/chemrxi… A Jupyter Notebook Tutorial Gallery: xuhuihuang.github.io/mlchem/html/ex…

🎁 Application fee waiver for all attendees! 📅 Oct 8 @ 10 AM ET 👉 Registration: us02web.zoom.us/meeting/regist… For more details about our program, visit: hass.rpi.edu/economics/econ…

A few scientific cover arts I created four journals recently — made possible by @Blender 's ability to handle biological models like protein structures and CT scans. #b3d

Generation of protein dynamics by machine learning 1. Machine learning, particularly generative models, is revolutionizing the prediction of protein dynamics by enabling the generation of structural ensembles beyond traditional simulations. This review highlights emerging approaches that capture protein dynamics in various forms, including PDB-like ensembles and acceleration of molecular simulations. 2. One significant innovation is the development of deep generative models (GMs) based on AlphaFold2, such as AlphaFlow and UFConf, which can generate multiple conformations from a single protein sequence. These models outperform traditional sampling methods in capturing PDB-like conformations. 3. The review emphasizes the importance of hybrid models that integrate experimental and simulation data. BioEmu, a diffusion model, demonstrates unprecedented performance in modeling both PDB and MD ensembles by leveraging a hybrid training strategy. This approach captures large and biologically significant conformational changes. 4. For non-globular proteins, especially intrinsically disordered regions (IDRs), ML methods are crucial for generating ensembles. Models like IDPFold and BioEmu show promise in capturing experimental observables of IDRs, such as chemical shifts and radius-of-gyration, using a combination of PDB structures and simulations. 5. The integration of experimental data directly into the generative process is another key advancement. Methods like DynamICE and DEERFold incorporate NMR and other experimental data during training, enhancing the accuracy of generated ensembles. This approach is essential for guiding ML models towards biologically relevant conformations. 6. Despite these advancements, challenges remain, including the transferability of models beyond training data and the generation of states with correct relative probabilities. The scarcity of long MD simulation datasets and the need for larger, more diverse training sets are also highlighted as critical areas for future work. 📜Paper: sciencedirect.com/science/articl… #MachineLearning #ProteinDynamics #StructuralBiology #GenerativeModels #Bioinformatics

Excellent work from @pranamanam. Looking forward to implement PepMLM on one our targets

Our first @DARPA program NODES is about to begin, come join us. Here is a glimpse of the broader vision. "A to-do list for realizing the sequence-to-function paradigm of proteins" #currentopinionsinstructuralbiology Despite AlphaFold's success, inferring function from sequence remains unsolved. This review lays out the roadmap to bridge this gap using: 🧠 Biophysical signatures 📉 Dimensionality reduction 🔬 Integrative modeling 💡 Machine learning beyond brute-force MD Key insight? We don’t need all dynamics — just the right representations. From MHC binding fingerprints to cell-scale simulations, this piece reframes the problem and offers a tractable path forward. 👉 Read more: sciencedirect.com/science/articl… Check out NODES - darpa.mil/research/progr… 🔍 #proteindynamics #AIinBiology #computationalbiology #structuralbio

NODES: Network of Optimal Dynamic Energy Signatures program aims to develop a groundbreaking deep learning tool, informed by the principles of biophysics, that can analyze vast numbers of protein sequences and predict their biological functions by identifying telltale patterns of protein movement. With this program, DARPA @DARPA seeks to characterize the function of potential biological threats within one hour, expanding threat assessment accuracy and speed exponentially. Speed is crucial for developing effective medical countermeasures for warfighters and for staying ahead of advancements in biotechnology that could lead to new and emerging biothreats. Additionally, NODES will provide a secure system for classifying and monitoring biological threats, which is vital for intelligence agencies to enhance bio-surveillance. youtu.be/QTbNKqUoum4?si… via @YouTube

the remarkable story behind this paper is best saved for another day when I can do it justice. for today, I thank the editor, who saw the potential in this work, and the reviewers, whose thorough evaluation of the paper helped improve it enormously

🚀 Super excited to share our new work, led by @SchnapkaV 🚀 bAIes, a Bayesian framework for generating accurate atomic-resolution ensembles of disordered & multi-domain proteins using #AlphaFold info! Now on @biorxivpreprint @ERC_Research biorxiv.org/content/10.110…

In a medical milestone, a customized base editor was developed, characterized in human and mouse cells, tested in mice, studied for safety in non-human primates, cleared by @US_FDA for clinical trial use, manufactured as a complex with an LNP, and dosed into a baby with a severe, rapidly progressing genetic disease... all in an astounding 7 months. Best of all, the infant patient shows apparent benefit. Congratulations to @kiranmusunuru, Rebecca Ahrens-Nicklas, and other team members for this heroic and inspiring effort, which has implications for the hundreds of millions of patients that suffer from thousands of genetic diseases. drive.google.com/file/d/1Jfku5j…

Drug discovery is *hard* and AI is not a silver bullet… endpts.com/ai-biotech-rec…

Students often get lost in complex frameworks of biology. Our animated lessons simplify concepts, saving time and clearing misconceptions! Learn more at smart-biology.com #biology #3D #science #EdTech

Today in @Nature we share our back-to-back stories with @nzhenguw revealing chemical-genetic convergence between a molecular glue & E3 ligase cancer mutations. 1/6

Machine Learning in Drug Discovery Resources page updated for 2025. github.com/PatWalters/res…

@tiwarylab Where is this? Looks like CP

@vijaypande, of a16z Bio+Health, recently gave us all a deep dive on deep learning. In his conversation with AI researcher and Professor at Stanford University @drfeifei, the two broke down the expansive world of AI at the intersection of bio, healthcare, and tech. Check out the full episode here: a16z.com/podcast/the-wo…

New post at Practical Cheminformatics, “Some Thoughts on Biotech vs Pharma for Computational Chemists”. practicalcheminformatics.blogspot.com/2023/12/some-t…

"PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction" Another sequence encoder with favorable downstream results arxiv.org/abs/2309.03099 github.com/ChakradharG/

This is India's biggest achievement in the 21st century. Period. h/t @Nabarun204