AIMC Topic: Drug Design

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Has Drug Design Augmented by Artificial Intelligence Become a Reality?

Trends in pharmacological sciences Oct 16, 2019
The application of artificial intelligence (AI) to drug discovery has become a hot topic in recent years. Generative molecular design based on deep learning is a particular an area of attention. Zhavoronkov et al. recently published a novel approach ...
Drug Design Drug Discovery Deep Learning Artificial Intelligence
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Models, Chemical Ligands Drug Design Molecular Conformation Drug Discovery Humans Proteins Quantitative Structure-Activity Relationship Neural Networks, Computer Small Molecule Libraries
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EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge.

Journal of chemical information and modeling Sep 3, 2019
Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline an...
Drug Repositioning Knowledge Bases Data Mining Software Humans Animals Drug Design Small Molecule Libraries Databases, Pharmaceutical
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...
Cyclin-Dependent Kinase 2 Drug Design Drug Discovery Models, Molecular Protein Kinase Inhibitors Thermodynamics Small Molecule Libraries Humans Machine Learning
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Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Journal of chemical information and modeling Aug 19, 2019
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learnin...
Molecular Docking Simulation Drug Design Drug Discovery Humans Machine Learning Ligands HIV Protease HIV Protease Inhibitors HIV Infections Protein Binding HIV-1
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Development and rigorous validation of antimalarial predictive models using machine learning approaches.

SAR and QSAR in environmental research Jul 22, 2019
The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against . Four different machine learning methods, namely the supp...
Quantitative Structure-Activity Relationship ROC Curve Machine Learning Area Under Curve Drug Design Antimalarials
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Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Proteins Databases, Protein Models, Molecular Deep Learning Cyclin-Dependent Kinase Inhibitor p16 Tumor Suppressor Protein p53 Binding Sites Proto-Oncogene Proteins c-mdm2 Matrix Metalloproteinase 13 Cyclin-Dependent Kinase 6 Mutation Databases, Pharmaceutical Machine Learning Ligands Proto-Oncogene Proteins c-bcl-2 Peptides Algorithms Drug Design Antineoplastic Agents
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

Chemical reviews Jul 11, 2019
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be ...
Machine Learning Algorithms Drug Design Drug Discovery Bayes Theorem Neural Networks, Computer Small Molecule Libraries Artificial Intelligence
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Deep Reinforcement Learning for Multiparameter Optimization in Drug Design.

Journal of chemical information and modeling Jul 5, 2019
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods f...
Molecular Structure Neural Networks, Computer Machine Learning Drug Discovery Drug Design Models, Theoretical Models, Molecular
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AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.

Journal of chemical information and modeling Jul 1, 2019
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that "one cannot ...
Molecular Structure Proteins Databases, Protein Machine Learning Algorithms Molecular Docking Simulation Drug Design Computational Biology
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