Biophysics sits at the fascinating intersection where the laws of physics meet the complexity of living systems. This field uses tools like light, electricity, and mechanical forces to decode how cells move, how proteins fold, and how our senses translate the world around us. Rather than just observing biology, biophysicists measure and model life to understand the fundamental machinery that powers every organism.

On Gist.Science, we make these discoveries accessible by curating the latest preprints directly from bioRxiv. Our team processes every new submission in this category, providing both clear, plain-language overviews and detailed technical summaries so readers of all backgrounds can grasp the cutting-edge science. Below are the most recent biophysics papers from bioRxiv, ready for you to explore.

⚛️ biophysics

Critical Assessment of ML models for ADMET Prediction in TDC leaderboards

This study critically evaluates the Therapeutics Data Commons (TDC) ADMET leaderboards and reveals that most top-ranked models suffer from irreproducibility, data leakage, or test-set overfitting, with only three methods passing rigorous verification, thereby highlighting the urgent need for stricter benchmarking standards including hidden test sets and standardized submission environments.

Koleiev, I., Stratiichuk, R., Shevchuk, N., Melnychenko, M., Nyporko, O., Todoryshyn, D., Husak, V., Starosyla, S., Yesy (…)2026-02-28
⚛️ biophysics

Impact of Image Representation on Deep Learning-Based Single-Cell Classification by Holographic Imaging Flow Cytometry

This study presents the first systematic evaluation of six image representation pipelines for deep learning-based single-cell classification in holographic imaging flow cytometry, revealing a clear trade-off between computational efficiency and accuracy while providing a Pareto-optimal framework to guide pipeline selection for biomedical applications.

Pirone, D., Cavina, B., Giugliano, G., Nanetti, F., Reggiani, F., Miccio, L., Kurelac, I., Ferraro, P., Memmolo, P.2026-02-28
⚛️ biophysics

QuantiTrack: A unified software to study protein dynamics in living cells

The authors present QuantiTrack, a user-friendly MATLAB-based software that provides an end-to-end solution for single-molecule tracking analysis in living cells, demonstrating its utility by revealing how hormone washout alters the binding dynamics and activation states of the glucocorticoid receptor.

Ball, D. A., Wagh, K., Stavreva, D. A., Hoang, L., Schiltz, R. L., Chari, R., Raziuddin, R., Mazza, D., Upadhyaya, A., H (…)2026-02-27
⚛️ biophysics

Time-Resolved Single-Molecule FRET Reveals Length-Dependent Nucleosome Decompaction by Poly(ADP-ribose)

By combining droplet-based microfluidic mixing with single-molecule FRET, this study reveals that poly(ADP-ribose) (PAR) triggers length-dependent nucleosome decompaction through electrostatic competition with DNA for histone binding, where only polymers exceeding ten ADP-ribose units efficiently drive rapid chromatin opening.

Yang, T., Gopi, S. R., Pinet, L., Simoni, S., Imhof, R., Nettels, D., Altmeyer, M., Best, R. B., Schuler, B.2026-02-27
⚛️ biophysics

Self-consistent automatic retrieval of single cell rotation enables highly reliable holo-tomographic flow cytometry

This paper presents a novel, fully automated, and self-consistent iterative optimization method that accurately retrieves the unknown rotation angles of single flowing cells, thereby significantly enhancing the reliability and scalability of Holo-Tomographic Flow Cytometry for label-free 3D refractive index tomography.

Pirone, D., Miccio, L., Bianco, V., Ferraro, P., Memmolo, P.2026-02-26
⚛️ biophysics

A Dynamic NMR Lineshape Simulation Framework for Lipid Diffusion and Membrane Thinning in Bicelles and Nanodiscs

This paper presents a comprehensive theoretical framework for simulating dynamic NMR lineshapes in lipid bicelles and nanodiscs that explicitly accounts for coupled lipid diffusion, orientational distributions, and membrane thinning, thereby enabling the quantitative interpretation of anisotropic interactions and membrane structural changes induced by biomolecular association.

Wi, S., Ramamoorthy, A.2026-02-26
⚛️ biophysics

AI-BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

The paper introduces AI-BioMech, a deep learning framework utilizing transfer learning with architectures like DeepLabv3 to directly predict the mechanical behavior of aperiodic biological cellular materials from 2D images with 99% accuracy, thereby eliminating the need for manual geometry definition and traditional finite element simulations.

Sadia, H., Dias, M. A., Alam, P.2026-02-25