Tootfinder

FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
Julian Cremer, Tuan Le, Mohammad M. Ghahremanpour, Emilia S{\l}ugocka, Filipe Menezes, Djork-Arn\'e Clevert
https://arxiv.org/abs/2510.02578

FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
We present Flowr.root, an equivariant flow-matching model for pocket-aware 3D ligand generation with joint binding affinity prediction and confidence estimation. The model supports de novo generation, pharmacophore-conditional sampling, fragment elaboration, and multi-endpoint affinity prediction (pIC50, pKi, pKd, pEC50). Training combines large-scale ligand libraries with mixed-fidelity protein-ligand complexes, followed by refinement on curated co-crystal datasets and parameter-efficient fine…

Computational Advances in Taste Perception: From Ion Channels to Neural Coding
Vladimir A. Lazovsky, Sergey V. Stasenko, Victor B. Kazantsev
https://arxiv.org/abs/2510.00010 htt…

Computational Advances in Taste Perception: From Ion Channels to Neural Coding
Recent advances in computational neuroscience demand models that balance biophysical realism with scalability. We present a hybrid neuron model combining the biophysical fidelity of Hodgkin-Huxley (HH) dynamics for taste receptor cells with the computational efficiency of Izhikevich spiking neurons for large-network simulations. Our framework incorporates biomorphic taste cell models, featuring modality-specific receptor dynamics (T1R/T2R, ENaC, PKD) and Goldman-Hodgkin-Katz (GHK)-driven ion cu…