AIMC Topic: Molecular Structure

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GuacaMol: Benchmarking Models for de Novo Molecular Design.

Journal of chemical information and modeling Mar 19, 2019
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural net...
Drug Design Deep Learning Models, Molecular Molecular Structure Pharmaceutical Preparations Benchmarking Isomerism Quantitative Structure-Activity Relationship Monte Carlo Method
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In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening.

Journal of chemical information and modeling Mar 5, 2019
Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks show promising results and often outperform traditional methods such as e...
Machine Learning Databases, Factual Retrospective Studies Bias Benchmarking Ligands Molecular Docking Simulation Structure-Activity Relationship Molecular Structure
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Models, Molecular Molecular Structure Pharmaceutical Preparations Machine Learning Drug Design Hydrogen Bonding Hydrophobic and Hydrophilic Interactions Molecular Conformation Quantitative Structure-Activity Relationship
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Imputation of Assay Bioactivity Data Using Deep Learning.

Journal of chemical information and modeling Feb 21, 2019
We describe a novel deep learning neural network method and its application to impute assay pIC values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and c...
Drug Discovery Deep Learning Biological Assay Molecular Structure Quantitative Structure-Activity Relationship Pharmaceutical Preparations Databases, Pharmaceutical
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Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Computational biology and chemistry Feb 16, 2019
BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline m...
Drug Screening Assays, Antitumor Structure-Activity Relationship Models, Molecular Molecular Structure Gallic Acid PC-3 Cells Cell Proliferation Support Vector Machine Dose-Response Relationship, Drug Time Factors Cell Line, Tumor Humans Artificial Intelligence Machine Learning Algorithms Antineoplastic Agents
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Identification of coenzyme-binding proteins with machine learning algorithms.

Computational biology and chemistry Jan 28, 2019
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on th...
Acyl Coenzyme A Molecular Structure Esters Software Humans Proteins Machine Learning Models, Molecular
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NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules.

Biomolecules Jan 24, 2019
Natural products (NPs) remain the most prolific resource for the development of smallmolecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates simil...
Molecular Structure Small Molecule Libraries Machine Learning Biological Products
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Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

Pharmaceutical research Jan 7, 2019
PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the i...
Humans Peripheral Nervous System Diseases Incidence Neural Networks, Computer Structure-Activity Relationship Drug Design Machine Learning Molecular Structure Neoplasms Antineoplastic Agents
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Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches.

Journal of biomolecular structure & dynamics Dec 24, 2018
Matrix metal proteinases-12 (MMP-12) is a hot pharmaceutical target on the treatment of many human diseases. There's a crying need for designing and finding new MMP-12 inhibitors. In this work, four machine learning approaches, support vector machine...
Molecular Structure Quantitative Structure-Activity Relationship Matrix Metalloproteinase Inhibitors Machine Learning Matrix Metalloproteinase 12 ROC Curve Algorithms Support Vector Machine Reproducibility of Results Drug Discovery
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De Novo Molecule Design by Translating from Reduced Graphs to SMILES.

Journal of chemical information and modeling Dec 21, 2018
A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep l...
Cheminformatics Databases, Pharmaceutical Models, Molecular Drug Design Molecular Structure Deep Learning Pharmaceutical Preparations
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