bioRxiv

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Hyperspectral Imaging to Quantify Nodules and Detect Biological Nitrogen Fixation in Legumes

bioRxiv Jan 1, 2025
Legume root nodules are important for biological nitrogen fixation, a process critical for plants to gain additional nitrogen from the environment. Nodule quantification is valuable for evaluating nitrogen fixation efficiency, assessing symbiotic rel...
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Digital Twin Approaches for Interpretable Side Effect Prediction in Drug Discovery

bioRxiv Jan 1, 2025
Artificial intelligence plays an ever-greater role in preclinical drug development, ranging from target identification and molecule design to ADME-Tox prediction; however, predicting side effects before performing clinical trials is still lagging beh...
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PyrMol: A Knowledge-Structured Pyramid Graph Framework for Generalizable Molecular Property Prediction

bioRxiv Jan 1, 2025
Accurate prediction of molecular properties is essential for accelerating drug discovery. While graph neural networks (GNNs) have achieved impressive progress, most models rely on atomic graphs with limited chemical semantics, leading to suboptimal g...
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Deep learning challenge to generate apo RNA conformations with cryptic binding site

bioRxiv Jan 1, 2025
RNA plays vital roles in diverse biological processes, thus drawing much attention as potential therapeutic target. Notably, cryptic ligand binding sites can be promising targets due to high specificity in RNA-ligand interaction. Structure Based Drug...
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Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

bioRxiv Jan 1, 2025
Interactions of long non-coding RNAs (lncRNAs) with proteins is responsible for numerous cellular processes, including transcriptional regulation, chromatin remodeling, cell differentiation, and intracellular signaling. In the past, numerous computat...
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qcGEM: a graph-based molecular representation with quantum chemistry awareness

bioRxiv Jan 1, 2025
The advancement of artificial intelligence (AI) has reshaped drug discovery. AI-based models typically rely on molecular representations for prediction. However, the absence of physically grounded information in mainstream molecular representations n...
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Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBench

bioRxiv Jan 1, 2025
Structure-based drug design (SBDD) has been profoundly reshaped by the advent of deep generative models, yet their practical impact on drug discovery remains limited. A central issue is the absence of a rigorous, application-oriented benchmark that m...
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SNNs Are Not Transformers (Yet): The Architectural Problems for SNNs in Modeling Long-Range Dependencies

bioRxiv Jan 1, 2025
Spiking neural networks (SNNs) have attracted growing interest for their ability to operate efficiently on low-power neuromorphic hardware, offering a biologically grounded route toward energy-efficient computation. However, despite advances in large...
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OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

bioRxiv Jan 1, 2025
Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more e...
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SLAy-ing oversplitting errors in high-density electrophysiology spike sorting

bioRxiv Jan 1, 2025
The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed wi...
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