Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required…
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48 results for “generic 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.
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.
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.
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 …
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.
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.
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 …
System automates identification of cancer drug repurposing from PubMed.
problem Manual extraction of cancer drug repurposing evidence from scientific publications is infeasible.
method NLP pipeline including querying, filtering, entity extraction, classification, and study type classification.
result Automated system extracts cancer drug repurposing evidence from PubMed abstracts.
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.
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.
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.
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.
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this manuscript, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1). the ranking positions of sensitive drugs an…
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 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively.
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.
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industriesquant-ph
Quantum machine learning boosts drug discovery efficiency.
problem Enhancing drug discovery through quantum computing.
method Quantum neural networks on gate-based quantum computers.
result Significant advancements in molecular property prediction and generation.
AI helps in drug discovery with understandable explanations.
problem Understanding the complex models behind AI-generated drugs.
method Explainable AI methods to interpret deep learning models.
result Improved interpretability of AI-generated drug properties.
Multilayer Network Analysis of the Drug Pipeline in the Global Pharmaceutical Industryphysics.soc-ph
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo…
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.
CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discoverycs.LG
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.
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.
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bi…
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.
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states befor…
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancerq-bio.QM
Develops a scalable model for drug combination prediction in cancer.
problem Accurate prediction of drug combinations for cancer treatment.
method Permutation invariant multi-output Gaussian Processes with variational approximation and deep generative model.
result Model efficiently borrows information across drug combinations and provides uncertainty quantification.
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics. Recent efforts in generation of large scale datasets profiling gene expression and drug sensitivity in cell lines have provided a unique opportunity to study this problem. However, one major challenge is the small number of sa…
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…
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.
Benchmark proposes to assess molecule docking efficiency.
problem Lack of realistic benchmarks for measuring progress in drug design.
method Proposes a docking-based benchmark using SMINA software.
result Graph-based generative models fail to generate high-scoring molecules.
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.
AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictionsq-bio.QM
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.
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.
Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Modelsq-bio.GN
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…
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.
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.
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …
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.
The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. H…
HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.
problem Inaccurate drug-target interaction prediction due to insufficient chemical information extraction.
method Hierarchical graph representation learning to extract chemical information from atoms, motifs, and molecules.
result HiGraphDTI outperforms state-of-the-art methods in DTI prediction and interaction interpretation.
Opioid overdose rates have reached an epidemic level and state-level policy innovations have followed suit in an effort to prevent overdose deaths. State-level drug law is a set of policies that may reinforce or undermine each other, and analysts have a limited set of tools for handling the policy collinearity using st…
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.
Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is one of the most important tasks in drug development and medical practice. Recently graph neural networks have achieved great success in this task by modeling drugs as nodes and drug-drug interactions as links and casting DDI predictions as link…
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these ar…