AIMC Topic: Drug Combinations

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The recent progress of deep-learning-based in silico prediction of drug combination.

Drug discovery today May 25, 2023
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In ...
Deep Learning Drug Combinations Algorithms
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Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology Apr 11, 2023
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the respon...
Drug Discovery Drug Combinations Machine Learning High-Throughput Screening Assays
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Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences Mar 21, 2023
Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance ...
Neural Networks, Computer Drug Combinations
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DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Molecules (Basel, Switzerland) Jan 14, 2023
Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interacti...
Drug Combinations Drug Interactions Combined Modality Therapy Neural Networks, Computer Gene Expression Profiling
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DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

Clinical pharmacokinetics Nov 11, 2022
BACKGROUND AND OBJECTIVE: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). T...
Algorithms Deep Learning Humans Machine Learning Drug Combinations Drug Interactions
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Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Journal of biomedical informatics Aug 17, 2022
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ...
Digoxin Hydrochlorothiazide Drug Combinations Natural Language Processing Machine Learning Phenotype Electronic Health Records Hypertension Humans
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MatchMaker: A Deep Learning Framework for Drug Synergy Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2022
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction e...
Drug Synergism Drug Combinations Humans Neoplasms Deep Learning Computational Biology
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Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Cancer letters Apr 29, 2021
Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy. Conventional strategies to overcome radioresistance involve understanding the underpinning molecul...
Humans Artificial Intelligence Neoplasms Radiation Oncology Radiation Tolerance Drug Discovery Drug Combinations
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TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

PLoS computational biology Feb 12, 2021
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...
Drug Synergism Drug Combinations Drug Evaluation, Preclinical Computational Biology Algorithms Precision Medicine Antineoplastic Agents Humans Computer Simulation Deep Learning Neural Networks, Computer Drug Discovery
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Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.

Chemical biology & drug design Oct 16, 2020
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...
Neural Networks, Computer Drug Interactions Humans Transcriptome Databases, Factual Drug-Related Side Effects and Adverse Reactions Drug Combinations
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