AIMC Topic: Drug Discovery

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What are the current challenges for machine learning in drug discovery and repurposing?

Expert opinion on drug discovery Mar 8, 2022
Drug Discovery Drug Repositioning Machine Learning Humans
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Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

Drug discovery today Mar 3, 2022
The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with th...
Deep Learning Proteins Drug Discovery Databases, Factual
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Accurate Physical Property Predictions via Deep Learning.

Molecules (Basel, Switzerland) Mar 3, 2022
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory ac...
Bayes Theorem Deep Learning Neural Networks, Computer Solubility Drug Discovery
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DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Methods (San Diego, Calif.) Feb 24, 2022
Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approve...
Deep Learning Neural Networks, Computer Proteins Drug Repositioning Drug Discovery
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Defining clinical outcome pathways.

Drug discovery today Feb 16, 2022
Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: mole...
Knowledge Drug Repositioning Drug Discovery Knowledge Bases
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.

Nature communications Feb 14, 2022
To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile rea...
Drug Discovery Biomedical Research Natural Language Processing Deep Learning Humans Reading Algorithms Electronic Data Processing Data Mining Information Storage and Retrieval Molecular Structure
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m6A modification: recent advances, anticancer targeted drug discovery and beyond.

Molecular cancer Feb 14, 2022
Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-r...
Adenosine Neoplasms Drug Discovery Prognosis Artificial Intelligence Humans
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Machine learning modeling of family wide enzyme-substrate specificity screens.

PLoS computational biology Feb 10, 2022
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their nat...
Amino Acid Sequence Substrate Specificity Proteins Drug Discovery Machine Learning
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Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

Nature protocols Feb 4, 2022
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking onl...
Drug Discovery Ligands Molecular Docking Simulation Artificial Intelligence Small Molecule Libraries
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SPP-CPI: Predicting Compound-Protein Interactions Based On Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three as...
Drug Discovery Neural Networks, Computer Humans Software Proteins
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