AIMC Topic: Drug Design

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Molecular diversity Apr 12, 2021
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design a...
Data Mining Quantitative Structure-Activity Relationship Chemistry Techniques, Synthetic Drug Development Support Vector Machine Models, Molecular Research Design Animals Drug Design Drug Discovery Deep Learning Big Data Artificial Intelligence Machine Learning Algorithms Humans
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A Novel Automated Screening Method for Combinatorially Generated Small Molecules.

Journal of chemical information and modeling Apr 12, 2021
A main challenge in the enumeration of small-molecule chemical spaces for drug design is to quickly and accurately differentiate between possible and impossible molecules. Current approaches for screening enumerated molecules (e.g., 2D heuristics and...
Machine Learning Drug Design
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Artificial intelligence in drug discovery: recent advances and future perspectives.

Expert opinion on drug discovery Apr 2, 2021
: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other ...
Drug Design Drug Discovery Quantitative Structure-Activity Relationship Humans Artificial Intelligence Machine Learning
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Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine Apr 1, 2021
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...
Databases, Pharmaceutical HIV Integrase Decision Trees Structure-Activity Relationship Drug Design Bayes Theorem Molecular Structure HIV Infections HIV-1 Computational Biology HIV Integrase Inhibitors Humans Machine Learning
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Prediction of activity cliffs on the basis of images using convolutional neural networks.

Journal of computer-aided molecular design Mar 19, 2021
An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure-activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein...
Neural Networks, Computer Drug Design Structure-Activity Relationship
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The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction.

Expert opinion on drug discovery Mar 19, 2021
INTRODUCTION: Artificial intelligence (AI) has seen a massive resurgence in recent years with wide successes in computer vision, natural language processing, and games. The similar creation of robust and accurate AI models for ADME/Tox endpoint and a...
Drug Discovery Humans Artificial Intelligence Drug Design
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GGL-Tox: Geometric Graph Learning for Toxicity Prediction.

Journal of chemical information and modeling Mar 15, 2021
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large libr...
Drug Design Proteins Machine Learning Algorithms
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Target2DeNovoDrug: a novel programmatic tool for -deep learning based drug design for any target of interest.

Journal of biomolecular structure & dynamics Mar 11, 2021
The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates and de...
Molecular Dynamics Simulation Ligands Drug Design Deep Learning Artificial Intelligence
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Nature biomedical engineering Mar 11, 2021
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrob...
Animals Drug Design Drug Discovery Deep Learning Microbial Sensitivity Tests Mice Anti-Bacterial Agents Drug Resistance, Bacterial Structure-Activity Relationship Female Molecular Dynamics Simulation Staphylococcus aureus Amino Acid Sequence Klebsiella pneumoniae Mice, Inbred BALB C Klebsiella Infections Antimicrobial Cationic Peptides Pseudomonas aeruginosa Escherichia coli Acinetobacter baumannii
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A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Molecular diversity Mar 9, 2021
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potentia...
Hypoglycemic Agents Molecular Structure Workflow Drug Discovery Protein Binding Binding Sites Dipeptidyl-Peptidase IV Inhibitors Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Hydrogen Bonding Neural Networks, Computer Artificial Intelligence Machine Learning Algorithms Molecular Docking Simulation Drug Design Humans
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