8 papers · ranked by Valyu relevance
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Tissue engineering and regenerative medicine applications require the integration of multifunctional materials, offering appropriate mechanical cues and geometric guidance to support tissue-specific cell differentiation. In this context, supramolecular gel noodles formed by ionic cross-linking of pre-assembled micellar…
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In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Rasha Atwi, Ye Wang, Simone Sciabola, Adam Antoszewski
Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and…
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Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
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Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Dimitris Gkoumas, Maria Liakata
The intersection of chemistry and Artificial Intelligence (AI) is an active area of research focused on accelerating scientific discovery. While using large language models (LLMs) with scientific modalities has shown potential, there are significant challenges to address, such as improving training efficiency and…
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
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The imperative to screen ultra-large chemical libraries necessitates high-throughput computational tools capable of efficiently leveraging all available structural and chemical information. We introduce UniDock-Pro, a unified platform built upon the GPU-accelerated Uni-Dock architecture, which integrates…