AIMC Topic: Drug Design

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Roughness of Molecular Property Landscapes and Its Impact on Modellability.

Journal of chemical information and modeling Sep 16, 2022
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property l...
Machine Learning Drug Design
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Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

International journal of molecular sciences Sep 8, 2022
In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a dee...
Biomarkers Carcinoma, Squamous Cell Humans Drug Discovery Deep Learning Drug Design Biomarkers, Tumor Gefitinib Gene Expression Regulation, Neoplastic NF-kappa B Squamous Cell Carcinoma of Head and Neck Systems Biology Head and Neck Neoplasms Metformin Mouth Neoplasms
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Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

Journal of chemical information and modeling Sep 7, 2022
In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Diff...
Ligands Static Electricity Drug Design Machine Learning
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Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

Journal of chemical information and modeling Jul 6, 2022
In recent years, molecular deep generative models have attracted much attention for its application in drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning...
Proteins Ligands Models, Molecular Neural Networks, Computer Drug Design
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Memory augmented recurrent neural networks for de-novo drug design.

PloS one Jun 23, 2022
A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output. Memory augmentation allows t...
Machine Learning Neural Networks, Computer Drug Design
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Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.

Journal of chemical information and modeling Jun 17, 2022
Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative ...
Drug Discovery Drug Design Models, Molecular Deep Learning
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Retro Drug Design: From Target Properties to Molecular Structures.

Journal of chemical information and modeling Jun 2, 2022
To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods...
Molecular Structure Machine Learning Artificial Intelligence Drug Discovery Drug Design Humans
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Modern machine learning for tackling inverse problems in chemistry: molecular design to realization.

Chemical communications (Cambridge, England) Apr 28, 2022
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on comb...
Bayes Theorem Drug Discovery Deep Learning Machine Learning Drug Design
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Machine learning approaches and their applications in drug discovery and design.

Chemical biology & drug design Apr 23, 2022
This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug ...
Drug Discovery Ligands Drug Design Artificial Intelligence Molecular Docking Simulation Machine Learning
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Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

Journal of chemical theory and computation Apr 1, 2022
The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit t...
Thermodynamics Solvents Drug Design Deep Learning
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