AI models predict new opioid ligands from molecular dynamics.
problem Lack of crystal structures limits virtual screening of drug candidates.
method Molecular dynamics simulation and machine learning.
result Identified a novel opioid chemotype.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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37 results for “opioid receptors”
Study identifies diverse health states of opioid users to improve policy.
problem Diverse health states of opioid users lead to ineffective policy interventions.
method Probabilistic topic modeling of medical histories.
result Learned phenotypes predict future opioid use and prescription variability.
Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learningcs.LG
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
CASTNet forecasts opioid overdoses using crime patterns.
problem Forecasting opioid overdose occurrences.
method Community-attentive spatio-temporal networks incorporating multi-head attention.
result Superior forecasting performance and interpretable community contributions.
The study identifies patient subgroups with enhanced or diminished opioid treatment effects.
problem Lack of prescribing guidelines for opioids leading to adverse outcomes.
method Generative model using mixture distribution and sparsity to discover subgroups with treatment effects.
result Human-interpretable insights on subgroups with enhanced or diminished treatment effects.
Paper uses GAN to predict opioid relapse from social media data.
problem Accurate relapse prediction for opioid addiction.
method Generative Adversarial Networks (GAN) model trained on sentiment images and social influences.
result GAN model predicts relapse better than alternatives, showing relapse is linked to joy and negative emotions.
Method infers TCR effects on disease using patient data.
problem Estimating causal effects of TCR sequences on patient outcomes.
method Corrects for confounders using pre-selection TCR data and neural-network estimator.
result Identifies potentially therapeutic TCRs for COVID-19 severity.
Paper addresses measurement error in observational data, estimating effects of maternal smoking and opioid use on childhood obesity.
problem Systematic bias in inferences from observational datasets due to measurement error.
method Missing data view of measurement error problem; marginalizes latent true exposure; identifies outcome distribution under specific conditions.
result Method estimates effects of maternal smoking and opioid use on childhood obesity using only subject-reported data, refining estimates and consistency with existing literature.
MINN-SA enhances cancer detection using TCR sequences with better interpretability.
problem Challenges in detecting cancers using TCR sequences due to one-to-many correspondence.
method Multiple Instance Neural Networks based on Sparse Attention (MINN-SA).
result MINN-SA achieves highest AUC scores on 10 cancer types compared to existing MIL approaches.
Model predicts drug overdose hotspots using EMS and toxicology data.
problem Predicting drug overdose hotspots to focus limited services.
method Spatial-temporal point process model integrating EMS and toxicology data.
result Model improves prediction accuracy by integrating heterogeneous data.
Social network analysis (SNA), which is a research field describing and modeling the social connection of a certain group of people, is popular among network services. Our topic words analysis project is a SNA method to visualize the topic words among emails from Obama.com to accounts registered in Columbus, Ohio. Base…
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
problem Improving single-cell RNA sequencing clustering by incorporating cell-cell communication.
method Integrates cell-cell communication into a variational autoencoder framework.
result Empirical results show CCCVAE outperforms standard VAEs in clustering performance.
DESMILES uses deep learning to improve drug discovery by optimizing molecule properties.
problem Improving the efficiency and accuracy of drug discovery through better molecular design.
method DESMILES is a deep neural network model that optimizes molecular properties for drug discovery.
result DESMILES achieved a 77% lower failure rate in modifying molecules to inhibit the dopamine receptor D2 compared to state-of-the-art models.
Study uses machine learning to analyze state drug policies and reduce overdose deaths.
problem Epidemic opioid overdose rates and ineffective state-level policies.
method Hierarchical clustering of 138 binomial variables to generate policy bundles, then regression analysis.
result Balancing certain policies leads to reduced overdose deaths, but only after second year.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
A novel circuit motif uses sister cells for inference with correlated priors.
problem Structured priors in neural systems pose architectural challenges.
method Proposes a novel circuit motif using sister cells to implement correlated priors without direct interactions.
result Demonstrates the efficacy of correlated priors for inference in noisy environments.
Detects anomalous patterns in non-independent data streams.
problem Low detection power for subtle, emerging irregularities in non-iid data.
method Combines Gaussian processes with subset scanning techniques.
result Powerful, interpretable methods for anomalous pattern detection.
A new method helps novices automatically set AutoDock's hyperparameters.
problem Helping novice users set optimal AutoDock hyperparameters.
method Surrogate based multi-objective algorithm using radial basis function model and non-dominated sorting.
result The method effectively helps novices set AutoDock hyperparameters.
Generative autoencoders help create new molecular structures.
problem Generating novel molecules with desirable properties.
method Used autoencoders to map molecule structures into a latent space and back.
result The latent space preserves chemical similarity and can generate new compounds.
We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced repr…
Proposes a linear dimension reduction method for high-dimensional classification.
problem High-dimensional classification with unequal covariance matrices.
method Simultaneous variable selection and linear dimension reduction followed by quadratic discriminant analysis.
result The method doesn't require estimating precision matrices and scales linearly with the number of measurements.
Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA …
Development and evaluation of a deep learning model for protein-ligand binding affinity predictionstat.ML
Deep learning predicts protein-ligand binding affinity with high accuracy.
problem Predicting protein-ligand binding affinity for drug discovery.
method 3D convolutional neural network trained on CASF and Astex Diverse Set benchmarks.
result Deep learning model outperformed classical scoring functions.
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
Machine learning detects drug overdose trends, aiding prevention.
problem Detecting subtle overdose patterns in spatio-temporal data.
method Gaussian Process Subset Scan and Multidimensional Tensor Scan.
result Identifies previously unknown overdose patterns and demographic clusters.
A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agencyq-bio.NC
A new hierarchy quantifies agency in systems based on information processing.
problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.
CORE optimizes molecules by copying or generating substructures, improving accuracy.
problem Inaccurate substructure prediction in molecule optimization.
method Copy & Refine (CORE) strategy combining scaffolding tree generation and adversarial training.
result Significant improvement in various molecule optimization metrics.
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
Deep generative model discovers inhibitors for unknown targets.
problem Discovering novel inhibitor molecules for unknown drug targets.
method Deep generative framework trained on protein sequences, small molecules, and interactions.
result Micromolar-level inhibition observed for two out of four synthesized candidates, including activity against SARS-CoV-2 variants.
PADME predicts drug-target interaction strengths using deep learning.
problem Challenges in drug-target interaction prediction, especially for cold-target problems.
method PADME uses deep neural networks to predict real-valued interaction strengths between compounds and proteins, handling cold-target problems.
result PADME consistently outperforms baseline methods on multiple datasets, including the ToxCast dataset.
Paper proposes a machine learning-based method for estimating mediation effects.
problem Challenges in estimating mediation effects with multiple, continuous mediators.
method Developed a one-step estimation algorithm using machine learning and Riesz learning.
result Proposed method can estimate mediation effects from just two statistical estimands.
Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.
problem Predicting anticancer compound sensitivity with high accuracy and interpretability.
method Multimodal attention-based convolutional encoder using SMILES, gene expression profiles, and protein-protein interaction networks.
result The model significantly outperforms baseline models and demonstrates high interpretability.
NetBiTE predicts drug sensitivity and identifies biomarkers in cancer.
problem Predicting drug sensitivity and identifying biomarkers in cancer.
method NetBiTE combines prior knowledge and gene expression data using a biased tree ensemble approach.
result NetBiTE outperforms RF in predicting IC50 drug sensitivity for drugs targeting membrane receptor pathways.
SNeCT integrates multi-platform genomic data using Tucker decomposition with network constraints.
problem Integrative analysis of large-scale, high-dimensional, sparse genomic data with prior knowledge incorporation.
method Parallel stochastic gradient descent on a network-constrained optimization function.
result Decomposed factor matrices stratify cancers, find similar patients, and personalize interpretation.
QSAR models struggle to predict activity cliffs, but graph isomorphism features improve AC-sensitivity.
problem QSAR models struggle to predict activity cliffs (ACs).
method Nine distinct QSAR models combining molecular representation methods and regression techniques.
result Graph isomorphism features improve AC-sensitivity.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
problem Designing efficient drugs for novel viral proteins.
method End-to-end framework combining VAE, controlled sampling, and predictors.
result Highly selective and affinity molecules for SARS-CoV-2 targets.
Exponential neural networks store many patterns, mapping cues to targets.
problem Storing many patterns in a neural network efficiently and accurately.
method Introduced an exponential neural network with multiple layers, each storing a dataset.
result The network can store an exponential number of patterns, and it generalizes well to unseen data.