scholarly journals Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach

RSC Advances ◽  
2020 ◽  
Vol 10 (40) ◽  
pp. 23834-23841
Author(s):  
Zong-Rong Ye ◽  
I.-Shou Huang ◽  
Yu-Te Chan ◽  
Zhong-Ji Li ◽  
Chen-Cheng Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Molecules ◽  
Fluorescence Emission ◽  
Emission Wavelength ◽  
Learning Approach ◽  
Computationally Efficient ◽  
Machine Learning Approach ◽  
Efficient Prediction

The combinatorial QSAR and machine learning approach provides the qualitative and computationally efficient prediction for fluorescence emission wavelength of organic molecules.

Machine Learning Approach for Predicting Nucleophilicity of Organic Molecules

2021 ◽  
Author(s):  
Vaneet Saini ◽  
Aditya Sharma ◽  
Dhruv Nivatia
Keyword(s):  
Machine Learning ◽  
Experimental Determination ◽  
Organic Molecules ◽  
Chemical Reactivity ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Intensive Approach

Nucleophilicity provides an important information about chemical reactivity of organic molecules. Experimental determination of nucleophilicity parameter is tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that...

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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