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Works21 from public data
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- Pseudo-margin-based universal domain adaptation23
An improved universal adaptation network (I-UAN) is proposed to perform domain alignment on the commonly-labeled samples by incorporating the pseudo-margins of target samples to estimate each source class’s probability belonging to the common label set.
- Metric-learning-assisted domain adaptation14
A novel metric-learning-assisted domain adaptation (MLA-DA) method, which employs a novel triplet loss for helping better feature alignment and explores the relationship between the second largest probability of a target sample's prediction and its distance to the decision boundary.
- AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins10
A novel algorithm named adversarial feature sub space enhancement (AFSE), which dynamically generates abundant representations in new feature subspace via bi-directional adversarial learning, and then minimizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs and significantly enhance the generalization of DGL models in predicting ligand bioactivities.
- RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases10
A new method is proposed, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities for better model interpretability in ligand-based virtual screening of drug leads from large compound databases.
- Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention9
A unique benchmark dataset is established for evaluating bioactivity prediction models in the context of protein-ligand complexes, showcasing the superior performance of the DTIGN method (with an average improvement of 27.03%) through comparison with 9 leading deep learning-based bioactivity prediction methods.
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- Gaps between medical biology and AI drug discovery4
This work proposes a biologically contextualized AI framework and provides guidelines for researchers in both medical biology and AI drug discovery, highlighting three crucial gaps in AI-driven drug discovery.
- OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs3
A novel attentive graph reconstruction neural network and ligand optimization scheme are proposed, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands within activity cliffs of activity cliffs.
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- Modeling of Red Blood Cells in Capillary Flow Using Fluid–Structure Interaction and Gas Diffusion2
A gas diffusion model and the immersed finite element method were used to simulate the gas diffusion into deformable RBCs running in capillaries, and it was discovered that when R BCs are deformed, the CO flux across the membrane becomes nonuniform, resulting in a reduced capacity for diffusion.
- Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets1
LigoSpace introduces GeoREC to quantify atomic-level empty space and Union-Pocket to unify multiple protein pockets, providing a global view of binding sites and employs a pairwise loss instead of commonly used MSE loss, to better capture relative relationships critical for drug discovery.
- Discriminating single-molecule binding events from diffraction-limited fluorescence1
A Temporal-to-Context Convolutional Neural Network (T2C CNN), which integrates long-term spatial convolutions, shallow cross-connected blocks, and a pooling-free structure to enhance contextual representation while preserving fine-grained temporal features is proposed.
- An open unified deep graph learning framework for discovering drug leads1
An open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework and will enhance the efficiency and productivity of drug discovery researchers.
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- Cell Membrane Biophysics as a Therapeutic Interface for Nanomedicine: From Disease-Associated Remodeling to Translational Qualification–
This work frames cell membrane biophysics as a therapeutic interface for nanomedicine and assesses how disease-associated membrane remodeling can create candidate therapeutic entry points and delivery barriers across cancer, neurodegeneration, inflammation, infection, and vascular disease.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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