AIMC Topic: Structure-Activity Relationship
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Sequence-to-function deep learning frameworks for engineered riboregulators.
Nature communications
Oct 7, 2020
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....
Prediction of Promiscuity Cliffs Using Machine Learning.
Molecular informatics
Sep 29, 2020
Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identifie...
Selecting machine-learning scoring functions for structure-based virtual screening.
Drug discovery today. Technologies
Sep 19, 2020
Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover...
Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data.
Molecules (Basel, Switzerland)
Aug 29, 2020
Activity landscape (AL) models are used for visualizing and interpreting structure-activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different t...
Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.
ACS combinatorial science
Aug 17, 2020
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...
Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example.
Pharmacological research
Jul 17, 2020
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle...
Polypharmacology
Dyspepsia
Animals
Male
Rats, Sprague-Dawley
Humans
Transcriptome
Proteome
Gene Expression Profiling
Cluster Analysis
Protein Interaction Maps
Systems Biology
Signal Transduction
Unsupervised Machine Learning
Gene Regulatory Networks
Structure-Activity Relationship
Duodenum
Medicine, Chinese Traditional
Proteomics
Molecular Structure
Drugs, Chinese Herbal
Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.
Pharmaceutical research
Jul 13, 2020
PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.
Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.
Molecules (Basel, Switzerland)
May 25, 2020
In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combinatio...
Matched Molecular Series Analysis for ADME Property Prediction.
Journal of chemical information and modeling
May 5, 2020
Generation and prioritization of new molecules are the most central part of the drug design process. Matched molecular series analysis (MMSA) has recently been proposed as a formal approach that captures both of these key elements of design. In order...
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions.
Journal of computer-aided molecular design
May 2, 2020
Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models re...
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