Results 101 to 110 of about 56,136 (229)

Transformer Learning in Sequence‐Based Drug Design Depends on Compound Memorization and Similarity of Sequence‐Compound Pairs

Molecular Informatics, Volume 45, Issue 1, January 2026.
Transformer model. Shown is a schematic representation of an encoder‐decoder transformer trained for protein sequence‐based compound design. Chemical language models (CLMs), particularly encoder‐decoder transformers, have advanced generative molecular design.
Jürgen Bajorath
wiley   +1 more source

Highlights of Model Quality Assessment in CASP16

Proteins: Structure, Function, and Bioinformatics, Volume 94, Issue 1, Page 314-329, January 2026.
ABSTRACT Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy (EMA) category featured both global and local quality estimation for multimeric assemblies (QMODE1 and ...
Alisia Fadini, Recep Adiyaman, Shaima N. Alhaddad, Behnosh Behzadi, Jianlin Cheng, Xinyue Cui, Nicholas S. Edmunds, Lydia Freddolino, Ahmet G. Genc, Fang Liang, Dong Liu, Jian Liu, Quancheng Liu, Liam J. McGuffin, Pawan Neupane, Chunxiang Peng, David R. Shortle, Meng Sun, Haodong Wang, Qiqige Wuyun, Guijun Zhang, Xuanfeng Zhao, Wei Zheng, Randy J. Read   +23 more
wiley   +1 more source

Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16

Proteins: Structure, Function, and Bioinformatics, Volume 94, Issue 1, Page 183-191, January 2026.
ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement.
Ryota Ashizawa, Sergei Kotelnikov, Omeir Khan, Stan Xiaogang Li, Ernest Glukhov, Xin Cao, Maria Lazou, Ayse Bekar‐Cesaretli, Derara Hailegeorgis, Veranika Averkava, Yimin Zhu, George Jones, Hao Yu, Dmytro Kalitin, Darya Stepanenko, Kushal Koirala, Taras Patsahan, Dmitri Beglov, Mark Lukin, Diane Joseph‐McCarthy, Carlos Simmerling, Alexander Tropsha, Evangelos Coutsias, Ken A. Dill, Dzmitry Padhorny, Sandor Vajda, Dima Kozakov   +26 more
wiley   +1 more source

Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them

WIREs Computational Molecular Science, Volume 16, Issue 1, January/February 2026.
The scientist is hiking towards the treasure of accurate and predictive simulations of relevant phenomena. Molecular Dynamics shows a path riddled with obstacles such as accuracy, speed or sampling issues. Deep‐learning offers a way around these obstacles, but runs into hurdles of its own.
Klara Bonneau, Aldo S. Pasos‐Trejo, Michael Plainer, Luca Sagresti, Jacopo Venturin, Iryna Zaporozhets, Alessandro Caruso, Edoardo Rolando, Andrea Guljas, Leon Klein, Maximilian Schebek, Filippo Albani, Raquel López‐Ríos de Castro, Zakariya El Machachi, Lorenzo Giambagli, Cecilia Clementi   +15 more
wiley   +1 more source

Equivariant Conley index versus degree for equivariant gradient maps

Discrete and Continuous Dynamical Systems - Series S, 2012
In this article we study the relationship between the degree forinvariant strongly indefinite functionals and the equivariantConley index. We prove that, under certain assumptions, achange of the equivariant Conley indices is equivalent to thechange of the degrees for equivariant gradient maps. Moreover, weformulate easy to verify sufficient conditions
Sławomir Rybicki, Anna Gołębiewska
openaire   +1 more source

The Necessary Uniformity of Physical Probability

Philosophy and Phenomenological Research, Volume 112, Issue 1, Page 290-306, January 2026.
ABSTRACT According to contemporary consensus, physical probabilities may be “non‐uniform”: they need not correspond to a uniform measure over the space of physically possible worlds. Against consensus, I argue that only uniform probabilities connect robustly to long‐run frequencies.
Ezra Rubenstein
wiley   +1 more source

Equivariant harmonic maps depend real analytically on the representation [PDF]

, 2021
Ivo Slegers
openalex   +1 more source

Splitting the difference: Computations of the Reynolds operator in classical invariant theory

Bulletin of the London Mathematical Society, Volume 58, Issue 1, January 2026.
Abstract If G$G$ is a linearly reductive group acting rationally on a polynomial ring S$S$, then the inclusion SG↪S$S^{G} \hookrightarrow S$ possesses a unique G$G$‐equivariant splitting, called the Reynolds operator. We describe algorithms for computing the Reynolds operator for the classical actions as in Weyl's book.
Aryaman Maithani
wiley   +1 more source

Stability and equivariant maps

Inventiones Mathematicae, 1989
Let G be a reductive algebraic group acting (linearizably) on projective varieties X and Y, and let \(\pi:\quad Y\to X\) be a G-equivariant morphism. The author defines a suitable linearization of the G-action on Y and then compares stability in X and Y. His most general result is that \(q\in Y\) is unstable (resp.
openaire   +2 more sources

Scissors congruence K$K$‐theory for equivariant manifolds

Bulletin of the London Mathematical Society, Volume 58, Issue 1, January 2026.
Abstract We introduce a scissors congruence K$K$‐theory spectrum that lifts the equivariant scissors congruence groups for compact G$G$‐manifolds with boundary, and we show that on π0$\pi _0$, this is the source of a spectrum‐level lift of the Burnside ring‐valued equivariant Euler characteristic of a compact G$G$‐manifold.
Mona Merling, Ming Ng, Julia Semikina, Alba Sendón Blanco, Lucas Williams   +4 more
wiley   +1 more source