AIMC Topic: Drug Design

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Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation.

Journal of chemical information and modeling Apr 9, 2020
drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based design tools have been successfully applied in the disco...
Humans Models, Molecular Enzyme Inhibitors Molecular Conformation Computer Simulation Drug Design Machine Learning
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The power of deep learning to ligand-based novel drug discovery.

Expert opinion on drug discovery Mar 31, 2020
INTRODUCTION: Deep discriminative and generative neural-network models are becoming an integral part of the modern approach to ligand-based novel drug discovery. The variety of different architectures of neural networks, the methods of their training...
Humans Drug Discovery Algorithms Deep Learning Ligands Neural Networks, Computer Drug Design
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Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.

Stroke and vascular neurology Mar 29, 2020
The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures ...
Humans Artificial Intelligence Molecular Targeted Therapy Protein Conformation Data Mining Ligands Proteins Drug Design Databases, Protein Molecular Structure Animals Pharmaceutical Preparations Drug Discovery Computer-Aided Design Big Data Structure-Activity Relationship
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How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques.

Molecules (Basel, Switzerland) Mar 23, 2020
A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able ...
Drug Design Drug Discovery Models, Chemical Databases, Chemical Deep Learning
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Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Journal of medicinal chemistry Mar 20, 2020
Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects...
Neural Networks, Computer Drug Design Organic Chemicals Chemistry, Pharmaceutical Databases, Chemical Factor Xa Inhibitors Datasets as Topic
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Improving detection of protein-ligand binding sites with 3D segmentation.

Scientific reports Mar 19, 2020
In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks espe...
Binding Sites Proteins Ligands Machine Learning Software Neural Networks, Computer Drug Design Drug Discovery Protein Binding
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A Review on Applications of Computational Methods in Drug Screening and Design.

Molecules (Basel, Switzerland) Mar 18, 2020
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecul...
Drug Design Drug Discovery Molecular Dynamics Simulation Machine Learning Molecular Docking Simulation
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Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method.

Journal of computer-aided molecular design Mar 17, 2020
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...
Quantum Theory Ligands Metals Protein Binding Proteins Databases, Protein Computational Biology Machine Learning Drug Design
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Exploring chemical space using natural language processing methodologies for drug discovery.

Drug discovery today Feb 3, 2020
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languag...
Humans Computer Simulation Chemistry, Pharmaceutical Drug Design Drug Discovery Natural Language Processing
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Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing.

Computational biology and chemistry Jan 24, 2020
Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the deve...
Computational Biology Escherichia coli DNA Gyrase Quantitative Structure-Activity Relationship Molecular Docking Simulation Topoisomerase II Inhibitors Machine Learning Quinolines Molecular Structure Anti-Bacterial Agents Drug Design Microbial Sensitivity Tests
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