AIMC Topic: Small Molecule Libraries

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A Deep Learning Approach to Antibiotic Discovery.

Cell Feb 20, 2020
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed pr...
Acinetobacter baumannii Databases, Chemical Mice, Inbred C57BL Mycobacterium tuberculosis Cheminformatics Thiadiazoles Clostridioides difficile Small Molecule Libraries Animals Mice Drug Discovery Mice, Inbred BALB C Machine Learning Anti-Bacterial Agents
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Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research Nov 18, 2019
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...
Evolution, Molecular Protein Kinases Animals Protein Binding Small Molecule Libraries Sequence Analysis, Protein Quantitative Structure-Activity Relationship Protein Kinase Inhibitors Humans Machine Learning Genes
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Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning.

Journal of computer-aided molecular design Nov 1, 2019
Small Molecule Libraries Computational Chemistry Machine Learning Drug Design Artificial Intelligence Humans
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Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.

Current protocols in chemical biology Sep 1, 2019
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in pr...
Pharmaceutical Preparations Inhibitory Concentration 50 Small Molecule Libraries Machine Learning Databases, Factual
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High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

Metallomics : integrated biometal science Mar 20, 2019
One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper. Recently, our group demonstrated that previousl...
Microbial Sensitivity Tests Staphylococcus aureus Bayes Theorem Copper Machine Learning Anti-Bacterial Agents High-Throughput Screening Assays Small Molecule Libraries
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Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

Advances in experimental medicine and biology Jan 1, 2016
High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more spec...
Photoreceptor Cells, Vertebrate Reproducibility of Results Green Fluorescent Proteins Animals Mice, Transgenic Cell Tracking Small Molecule Libraries Algorithms Computational Biology Phosphotransferases Primary Cell Culture Enzyme Inhibitors Machine Learning Neurites Cells, Cultured
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