AIMC Topic: Structure-Activity Relationship

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Selecting machine-learning scoring functions for structure-based virtual screening.

Drug discovery today. Technologies
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)
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
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...

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Pharmaceutical research
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)
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
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
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...

The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design.

Molecular informatics
The SAR Matrix (SARM) approach was originally conceived for the systematic identification of analog series, their structural organization, and graphical structure-activity relationship (SAR) analysis. For structurally related series, SARMs also produ...

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia.

Pharmacological research
Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data ...