AIMC Topic: Drug Design

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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 Humans Small Molecule Libraries 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 HIV Infections Ligands Protein Binding HIV Protease HIV-1 HIV Protease Inhibitors
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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...
Area Under Curve Antimalarials Quantitative Structure-Activity Relationship Machine Learning Drug Design ROC Curve
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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, ...
Databases, Pharmaceutical Matrix Metalloproteinase 13 Proto-Oncogene Proteins c-mdm2 Cyclin-Dependent Kinase 6 Machine Learning Algorithms Peptides Models, Molecular Mutation Ligands Proto-Oncogene Proteins c-bcl-2 Tumor Suppressor Protein p53 Binding Sites Cyclin-Dependent Kinase Inhibitor p16 Proteins Databases, Protein Drug Design Antineoplastic Agents Deep Learning
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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 ...
Small Molecule Libraries Artificial Intelligence Machine Learning Algorithms Drug Design Drug Discovery Bayes Theorem Neural Networks, Computer
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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...
Drug Design Drug Discovery Models, Theoretical Neural Networks, Computer Machine Learning Models, Molecular Molecular Structure
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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 ...
Algorithms Molecular Docking Simulation Drug Design Machine Learning Computational Biology Molecular Structure Proteins Databases, Protein
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Deep Learning in Chemistry.

Journal of chemical information and modeling Jun 13, 2019
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in t...
Chemical Phenomena Drug Design Quantitative Structure-Activity Relationship Cheminformatics Drug Discovery Humans Chemistry Techniques, Synthetic Deep Learning Neural Networks, Computer
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Machine Learning for Molecular Modelling in Drug Design.

Biomolecules Jun 4, 2019
Machine learning (ML) has become a crucial component of early drug discovery. This researcharea has been fueled by two main factors [...].
Models, Molecular Machine Learning Drug Design
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A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland) Jun 2, 2019
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...
Algorithms Molecular Dynamics Simulation Molecular Docking Simulation Drug Discovery Quantitative Structure-Activity Relationship Humans Ligands Binding Sites Protein Binding Receptor, Serotonin, 5-HT2A Receptors, G-Protein-Coupled Machine Learning Drug Design
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