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.
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.
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activi…
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.
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.
TIP model improves POSE prediction with less resources.
problem Predicting polypharmacy side effects from drug-protein interactions.
method TIP model operates on three subgraphs for progressive representation learning.
result Improves accuracy by 7%+, time efficiency by 83imes, and space efficiency by 3imes. 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.
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug thera-pies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corr…
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-o…
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
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.
We discovered secular trend bias in a drug effectiveness study for a recently approved drug. We compared treatment outcomes between patients who received the newly approved drug and patients exposed to the standard treatment. All patients diagnosed after the new drug's approval date were considered. We built a machine …
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…
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.
A new model uses GPs and latent force models to predict patient responses to drugs.
problem Challenges in modeling short-term effects of drugs on patient physiology.
method Hybrid Gaussian process with latent force model for joint modeling of patient physiology and drug effects.
result The model accurately predicts patient responses to three common drugs, showing competitive performance.
Paper introduces IC-index to evaluate interaction prediction methods.
problem Evaluate interaction prediction methods using IC-index.
method IC-index measures interaction direction prediction performance.
result IC-index complements existing prediction performance estimators.
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.
Machine learning identifies potential drugs for COVID-19.
problem Finding effective treatments for COVID-19.
method Trained neural network models on virus protein sequences and antiviral drugs.
result Identified potential drug candidates for treating COVID-19.
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.
Study improves drug synergy prediction using ensemble learning.
problem Predicting drug synergy in complex diseases.
method Investigated different compound representations and proposed an ensemble model.
result Ensemble model outperforms baseline models.
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
problem Complexity and limited data in modeling cardiac effects of drugs.
method Combining meta-learning with SBINNs to solve parameterized cardiac action potential models.
result hyperSBINN outperforms traditional solvers in speed and accuracy for predicting APD90 values.
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.
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. Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Predicting the occurrence of DDIs helps drug safety professionals allocate investigative resources and take appropriate regulatory action promptly. Traditional DDI prediction methods predict DDIs based on the similarity between …
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.
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.
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.
Generative model tailors anticancer drugs based on transcriptomic data.
problem Designing effective anticancer drugs considering genetic profiles.
method RL framework using pretrained VAEs to generate compounds conditioned on transcriptomic data.
result Generative model produces molecules with high predicted inhibitory effects.
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.
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.
Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similar…
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi…
Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment. However, current methods are only able to handle a very small proportion of the existing chemicals. We address this issue by proposing an integrative deep learning architecture that learns a …
New DTI model using self-attention molecule representation outperforms state-of-the-art.
problem Predicting drug-target interactions to reduce costs and improve personalized medicine.
method Proposes a new molecule representation using self-attention and a new DTI model.
result Our DTI model outperforms state-of-the-art by up to 4.9% points in precision-recall.
Generative Intervention Models predict perturbation effects without knowing the underlying mechanisms.
problem Predicting perturbation effects when the mechanisms are unknown.
method Generative Intervention Models (GIM) that map perturbation features to distributions over atomic interventions in a causal model.
result GIMs achieve robust out-of-distribution predictions and infer underlying perturbation mechanisms.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
problem Scaling high-throughput screens to measure cellular responses for many drugs is costly and challenging.
method ChemCPA, a new encoder-decoder architecture combined with transfer learning.
result Training on existing bulk RNA HTS datasets improves generalization performance, reducing the need for extensive single-cell screens.
Study repurposes open data to find potential COVID-19 drugs.
problem Developing effective treatments for COVID-19.
method Deep learning network-based approach using large scientific corpus.
result Identified 41 repurposable drugs for COVID-19.
The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially va…
NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.
problem Maintaining minimum pairwise distance between atoms to avoid separation violations in drug design.
method Enforces distance constraint between atomic nuclei and manifolds in a diffusion model.
result Reduces separation violations by up to 100.00% and enhances binding affinity by up to 22.16%.
Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional network…
Machine learning predicts NaV1.7 inhibitors, leading to effective drug K1.
problem Predicting and optimizing NaV1.7 inhibitors for therapeutic use.
method Machine learning, specifically RF-CDK model, for predicting and screening.
result RF-CDK model identified effective compound K1, verified by cell patch clamp.
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are …
Active learning has shown to reduce the number of experiments needed to obtain high-confidence drug-target predictions. However, in order to actually save experiments using active learning, it is crucial to have a method to evaluate the quality of the current prediction and decide when to stop the experimentation proce…
Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.
problem Cold-start regimes in drug-target affinity prediction due to label scarcity and domain shifts.
method Two-stage framework: latent manifold alignment and latent diffusion regularization.
result Significantly outperforms state-of-the-art baselines, especially in zero-shot generalization.
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-l…
A model learns symptom-drug relations for PD patients.
problem Automatic prescription recommendation for Parkinson's Disease patients.
method Builds a dataset of PD symptoms and prescriptions, learns latent symptom space, uses alternating optimization.
result Effective in recommending suitable prescription drugs for new PD patients.
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.