Machine learning approach to discovering cascade reaction patterns. Application to reaction pathways prediction.

J Chem Inf Model

Department of Physical Chemistry, Faculty of Chemistry, Rzeszow University of Technology, 35-959 Rzeszow, Poland.

Published: June 2009

We propose a combinatorial learning procedure for discovering graph transformation patterns based on combining transformations that can be used in a consecutive fashion. Application of this kind of pattern to a specified chemical system allows to combine a sequence of consecutive transformations into a one-step operation limiting the complexity of a reaction tree. In a retrosynthetic sense, it provides a global strategy for bond disconnections to plan more efficient convergent syntheses. In a forward direction the approach reduces the number of iterations in order to exploring the courses of complex multistep processes. The procedure enhances the capabilities and applications of the CSB (Chemical Sense Builder) computer program to assist in organic synthesis, biochemistry, or medicinal chemistry. As an example, we present the automatic derivation of the transformation pattern for Ugi-type four-component reactions (reaction sequences) and its application to assist in the designing new cascade transformations for diversity-oriented synthesis (DOS).

Download full-text PDF

Source
http://dx.doi.org/10.1021/ci9000597DOI Listing

Publication Analysis

Top Keywords

machine learning
4
learning approach
4
approach discovering
4
discovering cascade
4
reaction
4
cascade reaction
4
reaction patterns
4
patterns application
4
application reaction
4
reaction pathways
4

Similar Publications

Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.

BMC Pulm Med

January 2025

Universal Scientific Education and Research Network (USERN), Tehran, Iran.

Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.

View Article and Find Full Text PDF

Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.

View Article and Find Full Text PDF

Background: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understanding of risk and protective factors to enhance prevention efforts. This study investigated the key risk and protective factors most highly associated with adolescent bullying victimization.

View Article and Find Full Text PDF

Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.

View Article and Find Full Text PDF

Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!