Algorithm generates new drug molecules from prototypes, showing diversity and validity.
problem Designing new drugs from existing prototypes is expensive and time-consuming.
method Conditional Diversity Networks (CDN) for unsupervised generation of drug molecules.
result Generated molecules are valid and significantly different from prototypes, including FDA-approved drugs.
Generative model designs drug combinations for improved efficacy and reduced side effects.
problem Designing effective drug combinations to overcome resistance and reduce side effects.
method Developed a deep generative model using HVGAE and a novel reward system.
result Network-principled drug combinations show reduced toxicity and potential for new strategies.
Paper proposes a framework to predict therapeutic properties of compounds.
problem Predict therapeutic properties of compounds with heterogeneous data.
method Domain-adversarial multi-task framework using adversarial learning.
result Framework improves performance over competitive baselines.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
problem Predicting biological effects of chemical structures for drug discovery.
method Siamese Graph Convolutional Neural Networks for structure-biological effect mapping.
result Highly accurate predictions of biological effects for structurally dissimilar compounds.
Study improves drug prediction accuracy for pharmacokinetic parameters.
problem Limited accuracy in predicting pharmacokinetic parameters.
method Integrated transfer learning and multitask learning approach.
result Improved model generalization and predictive ability.
Study proposes a new approval policy for ML-based medical devices to prevent gradual performance degradation.
problem Gradual deterioration in machine learning model performance over time in medical devices.
method Formulated an automatic algorithmic change protocol (aACP) as an online hypothesis testing problem, considering both error-rate guarantees and non-guaranteed policies.
result Controlled the rate of gradual deterioration (biocreep) in machine learning models without significantly impacting approval of beneficial modifications.
Improved inter-scanner MS lesion segmentation through adversarial training.
problem Variability in MRI scanner or protocol differences affect automated lesion segmentation accuracy.
method Trained a CNN base model and a discriminator model adversarially on multi-scanner longitudinal data.
result Adversarial training improves inter-scanner consistency of lesion segmentations.
New methods predict drug interactions using drug co-medication patterns and graph matching.
problem Predicting adverse drug reactions from drug combinations.
method Developed novel kernels over drug combinations using support vector machines and graph matching to measure similarities.
result Achieved an AUC of 0.912 on a real-world dataset.
A new drug embedding method using hierarchical drug relations and chemical structures.
problem Learning accurate drug representations from chemical structures and hierarchies.
method Semi-supervised drug embedding using VAE in hyperbolic space.
result The method accurately places drugs in a hierarchy and predicts side-effects.
New method predicts drug interactions from drug images.
problem Predicting drug interactions from molecular structures.
method Siamese neural network using drug structure images.
result First work predicting DDIs from drug images.
New method predicts cancer drug rankings based on genomic data.
problem Selecting the right drugs for cancer patients.
method pLETORg method that predicts drug ranking structures using latent vectors.
result pLETORg significantly outperforms state-of-the-art methods in prioritizing new sensitive drugs.
Graph-augmented CNN predicts drug interactions with high accuracy.
problem Predicting drug-drug interactions (DDIs) with high accuracy.
method Combining graph CNN with an attentive pooling network to extract structural relations between drug pairs.
result Desirable performance with ROC 0.988, F1-score 0.956, and AUPR 0.986.
Models predict drug interactions with high accuracy.
problem Detecting drug-drug interactions to prevent medical injuries.
method Artificial neural networks and graph similarity measures.
result Models achieve high accuracy in predicting drug interactions.
Deep Rule Forests identifies drug-drug and drug-disease interactions causing AKI.
problem Identifying drug-drug and drug-disease interactions leading to AKI.
method Deep Rule Forests (DRF) algorithm discovering rules from multilayer tree models.
result DRF model outperforms other algorithms in prediction accuracy and interpretability.
CardiGraphormer uses SSL and GNNs to improve drug discovery.
problem Challenges in drug discovery due to combinatorial chemical space and limited approved drugs.
method Combines self-supervised learning, Graph Neural Networks, and Cardinality Preserving Attention.
result Enhanced predictive performance and interpretability in drug discovery.
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.
Method learns drug-disease representations for repositioning opportunities.
problem Identifying new uses for existing drugs.
method Multi-relation unsupervised graph embedding model.
result Superior prediction performance in repositioning opportunities.
Text classification on drug SMILES strings yields competitive drug type classification results.
problem Classifying drug types using conventional text classification methods.
method Treated drug SMILES as sentences and applied basic NLP methods for classification.
result Competitive drug type classification results achieved.
Bi-GNN models drug interactions using a bi-level graph approach.
problem Predicting drug-drug interactions using machine learning.
method Bi-level graph neural networks that consider both interaction graph and representation graphs of drugs.
result Bi-GNN model improves DDI prediction accuracy compared to existing methods.
MuLFA predicts drug interactions more accurately than existing methods.
problem Improving drug safety by predicting drug interactions.
method Proposes MuLFA, a factorization autoencoder that models nonlinear interactions between drug pairs.
result MuLFA outperforms state-of-the-art methods in predicting drug interactions.
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
Predict drug-drug side effects using co-attention neural network.
problem Early detection of polypharmacy side effects in drug combinations.
method Co-attention neural network architecture for DDI prediction.
result State-of-the-art results on predicting side effects from drug types and structures.
GENN predicts drug interactions by modeling correlations between link labels.
problem Predicting drug-drug interactions with consideration of link type correlations.
method GENN uses graph energy neural networks to model link type correlations in DDI prediction.
result GENN outperforms baseline models by 13.77% and 5.01% in PR-AUC on two real-world datasets.
Deep learning predicts synergistic drug combinations from multi-omics data.
problem Predicting effective drug combinations to overcome cancer drug resistance.
method AuDNNsynergy model integrating gene expression, copy number, genetic mutation data and drug properties.
result AuDNNsynergy model outperforms state-of-the-art approaches.
Dr.S recommends cancer drugs based on genomic data.
problem Personalizing cancer treatments using genomic information.
method Machine learning to identify optimal drug-gene associations.
result Developed a Drug Recommendation System (Dr.S) for cancer cell lines.
A neural network predicts drug interactions using attention mechanisms.
problem Predicting drug-drug interactions from massive combinations of drugs.
method Siamese self-attention multi-modal neural network integrating drug characteristics.
result The model achieves AUPR scores ranging from 0.77 to 0.92 on various benchmark datasets.
STNN-DDI predicts drug interactions using substructure-aware neural networks.
problem Predicting drug-drug interactions (DDIs) to avoid side effects in poly-drug treatments.
method Designing a novel Substructure-ware Tensor Neural Network (STNN-DDI) that learns a 3-D tensor of substructure-substructure interactions.
result Significant improvement in AUC, AUPR, Accuracy, and Precision compared to state-of-the-art models.
HAMN combines CF models to improve drug repositioning.
problem Efficient drug repositioning with cold start problem.
method Hybrid Attentional Memory Network (HAMN) integrating memory and attention mechanisms.
result HAMN outperforms other models in drug repositioning tasks.
Network medicine predicts repurposable drugs for COVID-19.
problem Identifying effective drugs for SARS-CoV-2 infections quickly.
method Artificial intelligence, network diffusion, and network proximity algorithms.
result A multimodal approach combining predictions from multiple algorithms outperforms individual methods.
Bayesian model for cancer drug studies maps dose-response curves.
problem Mapping dose-response curves in cancer drug studies.
method Bayesian Tensor Filtering (BTF) with low-dimensional embeddings and structured shrinkage priors.
result BTF outperforms state-of-the-art methods in cancer drug studies.
Paper introduces tCNNS model for predicting drug cell line interactions.
problem Predicting phenotypic drug responses on cancer cell lines.
method tCNNS model using SMILES format for drugs and cancer cell lines.
result Achieves 0.84 for R2 and 0.92 for Rp. REP predicts drug response at every stage of treatment using time-course gene expression data.
problem Lack of dynamic drug response prediction from time-course gene expression data.
method REP framework that predicts drug response values at every stage of a long-term treatment using recursive structure and tensor completion.
result REP can estimate drug response at any stage of a given treatment from initial gene expression levels.
CASTER predicts drug interactions using chemical substructures.
problem Identifying potential drug-drug interactions during drug design.
method CASTER uses sequential pattern mining, auto-encoding, and dictionary learning to predict DDIs.
result CASTER outperformed state-of-the-art models and provided interpretable predictions.
Paper proposes a model to integrate diverse drug features for accurate similarity measures.
problem Challenges in integrating heterogeneous, noisy, nonlinear-related drug features.
method Attentive Multi-view Graph Auto-Encoders with flexible design for semi-supervised and unsupervised settings.
result Significant predictive accuracy improvement and better interpretability.
Paper proposes a new method for predicting drug interactions using adversarial autoencoders.
problem Predicting drug interactions to prevent adverse events.
method Introduces adversarial autoencoders based on Wasserstein distances and Gumbel-Softmax relaxation to generate high-quality negative samples.
result Significant improvements in link prediction and DDI classification tasks.
Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.
problem Optimal insulin bolus decisions for type-I diabetes patients are not well-established.
method Applied Reinforcement Learning to simulated T1DM data.
result Optimal bolus rule differs from standard advisors and can prevent hypoglycemia.
Model predicts anti-cancer drug responses using gene and molecular data.
problem Expensive and time-consuming cancer drug discovery and tailoring.
method Uses variational autoencoders and multi-layer perceptrons to encode gene expression and drug data.
result High average R2 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively. Paper proposes an inductive RGCN for few-shot link prediction in drug-repurposing.
problem Predicting rare interactions in drug-repurposing for novel diseases.
method Proposes an inductive RGCN to learn relation embeddings for few-shot learning.
result Significantly outperforms state-of-the-art models in few-shot learning tasks.
GEFA predicts drug-target affinity using graph neural networks.
problem Accurate prediction of drug-target interactions for rapid drug repurposing.
method GEFA (Graph Early Fusion Affinity) is a novel graph-in-graph neural network with attention mechanism.
result GEFA effectively models drug-target interactions, demonstrating the effectiveness of pre-trained protein embedding and nested graph representation.
Improved drug response prediction using ensemble learning and gene expression signatures.
problem Predicting chemotherapeutic response of cancer cells to drugs.
method Combining machine learning methods and drug-induced gene expression signatures for improved performance.
result Ensemble method improves drug activity prediction accuracy.
Improves drug properties using a novel LLM and reinforcement learning.
problem Optimizing drug properties while retaining chemical stability.
method Structured Policy Optimization (SPO) for fine-tuning a large language model.
result Enhanced drug properties across multiple target objectives.
Study reveals gender and age biases in drug interactions, impacting public health costs.
problem Gender and age biases in drug interactions leading to adverse reactions and increased costs.
method City-wide longitudinal analysis of electronic health records (EHR) from Blumenau, Brazil.
result Women have a 60% increased risk of drug interactions compared to men, and this increases to 90% for major adverse drug reactions.
Dr.VAE improves drug response prediction accuracy.
problem Improving accuracy of drug response prediction.
method Two deep generative models based on Variational Autoencoders.
result Dr.VAE outperforms benchmarks by 3-11% AUROC and 2-30% AUPR.
Proposes a self-attention-based method for drug-target interaction prediction.
problem Interpreting machine learning models for drug-target interactions.
method Self-attention-based multi-view representation learning approach.
result Competitive prediction performance with biologically interpretable results.
Collaborative filtering predicts drug responses from gene expression data.
problem Predicting drug responses from large gene expression datasets with limited samples.
method Low-rank matrix factorization and latent linear regression.
result The proposed method outperforms state-of-the-art methods in predicting drug-gene associations.
Model predicts drug response in tumors using genomic profiles.
problem Challenges in translating genomic insights to tumor-specific drug response predictions.
method Deep neural network (DNN) model trained on mutation and expression profiles of cancer cell lines.
result Achieved mean squared error of 1.96 for predicting IC50 values of 265 drugs.
Visualizes deep generative models for drug design.
problem Limited visualization tools for deep generative models in drug discovery.
method Proposes a visualization framework for deep graph generative models.
result Interactive visualization and molecular optimization tools.
Deep learning predicts drug side-effects from molecular graphs.
problem Predicting drug side-effects from molecular structures.
method Recurrent Graph Neural Networks for multi-class multi-label graph-focused classification.
result Improved classification capability compared to previous methods.