12 papers · ranked by Valyu relevance
Eliezer Masliah
How transient neural representations become integrated and stable enough to function as internal neural models remains incompletely understood. Grounded in efficient coding, Bayesian and predictive frameworks, recurrent and attractor dynamics, neural state-space models, and systems neuroscience, the Principle of…
Steven M. Muskal, Malcolm J. McGregor
A three-dimensional pharmacophore fingerprint records the binding features a molecule can present. It is a description of a hand in search of a glove. Because it is defined by presented features instead of two-dimensional structure, it can identify pharmacophoric similarity between structurally distinct compounds…
Alp Tartici, Mihajlo Stojkovic, Anru Tian, Michael C. Jewett + 2 more
Protein engineering has important implications in the bioeconomy, enabling applications in materials, medicine, and energy. A key challenge is designing protein sequences that have a specific form and function. Protein inverse folding seeks to address this challenge by identifying amino acid sequences compatible with a…
Hayato Idei, Keisuke Suzuki, Yuichi Yamashita
Mindfulness has established psychological benefits, such as stress reduction and emotional regulation; however, the underlying computational mechanisms, particularly in relation to mind-wandering, remain unclear. This study aimed to present a hierarchical recurrent neural network-based agent model grounded in the free…
Nele P. Quast, Matthew I. J. Raybould, Charlotte M. Deane
The development of highly accurate deep learning models for protein structure prediction has transformed the landscape of T-cell receptor (TCR) structure data, which can now be accessed at repertoire scale. We provide a perspective on the growing field of structural TCR immunoinformatics, summarizing core principles of…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…
Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan
Protein-ligand pose prediction is central to structure-based drug discovery, yet the relative performance of physics-based and AI-driven methods under realistic cross-docking conditions remains insufficiently characterized. Here, we compare physics-based docking methods (AutoDock4, AutoDock Vina, and DOCK 6) with…
Kunyu Wang, Jon Paul Janet, Alessandro Tibo
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Ana Lúcia Leitão, Francisco J Enguita
The evolution of molecular visualization software represents a pivotal yet often underappreciated dimension of computational structural biology and bioinformatics. As the scale and complexity of structural datasets increase, spanning from cryo-electron microscopy reconstructions to proteome-wide AI-predicted models…
Yuesong Wu, Haohao Su, Yuehua Cui
Cell-cell communication (CCC) is essential for maintaining tissue organization and driving biological progression, yet its inference from transcriptomic data has long been limited by the absence of spatial context. Advances in spatial transcriptomics (ST) now enable mechanistically grounded analyses of CCC by…
Bach Tran Nguyen, Aurélie Barrail-Tran, Moreno Ursino, Sébastien Goutal + 6 more
First-in-human studies require dose extrapolation from pharmacokinetic animal studies combined with safety assessments. Subtherapeutic doses of radiolabelled drugs can be administered in preclinical and early clinical development to gain a dynamic pharmacokinetic understanding, potentially informing…
Polina Bombina, Kevin R. Coombes
Comparative studies of trajectory inference (TI) methods evaluate complete computational pipelines, making it impossible to isolate how much distortion is introduced specifically by the dimensionality reduction (DR) step. To our knowledge, no study has directly and systematically evaluated how well DR methods alone…