AIMC Topic: Drug Discovery

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New machine learning and physics-based scoring functions for drug discovery.

Scientific reports Feb 4, 2021
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different tar...
Humans Drug Discovery Support Vector Machine Internet Research Design Molecular Docking Simulation Software Entropy Protein Interaction Mapping Drugs, Investigational Datasets as Topic Protease Inhibitors Peptide Hydrolases Ligands Hydrophobic and Hydrophilic Interactions
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Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Molecular diversity Feb 3, 2021
Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 re...
ROC Curve Protein Binding Models, Molecular Reproducibility of Results Drug Discovery Cheminformatics Leukotriene Antagonists Receptors, Leukotriene Machine Learning Quantitative Structure-Activity Relationship Algorithms Binding Sites Molecular Structure
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SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences Jan 30, 2021
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...
Protein Domains Ligands Datasets as Topic Deep Learning Animals Protein Binding Humans Binding Sites Protein Structure, Secondary Drug Discovery Caenorhabditis elegans
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Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data.

Drug discovery today Jan 27, 2021
'Artificial Intelligence' (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce,...
Knowledge Bases Translational Science, Biomedical Biomedical Research Drug Discovery Humans Artificial Intelligence
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MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

Interdisciplinary sciences, computational life sciences Jan 27, 2021
An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In ...
Drug Discovery Mutagens Mutagenesis Neural Networks, Computer
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Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening.

Journal of chemical information and modeling Jan 26, 2021
Similarity-based virtual screening is a fundamental tool in the early drug discovery process and relies heavily on molecular fingerprints. We propose a novel strategy of generating domain-specific fingerprints by training neural networks on target-sp...
Molecular Structure Ligands Neural Networks, Computer Drug Discovery
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Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, for dr...
Drug Discovery Humans Deep Learning Computational Biology Neoplasms Genomics Antineoplastic Combined Chemotherapy Protocols
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Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Journal of pharmaceutical sciences Jan 23, 2021
Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which ...
Humans Animals Species Specificity Metabolic Clearance Rate Drug Elimination Routes Drug Discovery Pharmaceutical Preparations Rats Pharmacokinetics Deep Learning Models, Biological
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Use of artificial intelligence to enhance phenotypic drug discovery.

Drug discovery today Jan 20, 2021
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The resp...
Drug Discovery Drug Development Phenomics Molecular Targeted Therapy Humans Artificial Intelligence Machine Learning
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Integration of AI and traditional medicine in drug discovery.

Drug discovery today Jan 18, 2021
AI integration in plant-based traditional medicine could be used to overcome drug discovery challenges.
Phytotherapy Medicine, Traditional Artificial Intelligence Drug Discovery Humans
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