Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling

2021 ◽  
Author(s):  
Jiachen Yan ◽  
Xiliang Yan ◽  
Song Hu ◽  
Hao Zhu ◽  
Bing Yan
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Molecular Modeling ◽  
Acetylcholinesterase Inhibition

Chemoinformatics Profiling of Ionic Liquids Cytotoxicity—From Machine Learning to Network-Like Similarity Graphs

2015 ◽  
Author(s):  
Maykel Cruz-Monteagudo ◽  
Eduardo Tejera ◽  
Cesar Paz-y-Miño ◽  
Yunierkis Perez-Castillo ◽  
Aminael Sánchez-Rodríguez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ionic Liquids

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Rate Constants ◽  
Learning Approaches ◽  
Learning Models ◽  
Organic Reaction ◽  
Machine Learning Models ◽  
Selection Of

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

2021 ◽  
Vol 61 (9) ◽  
pp. 4266-4279 ◽  
Author(s):  
Kuo Hao Lee ◽  
Andrew D. Fant ◽  
Jiqing Guo ◽  
Andy Guan ◽  
Joslyn Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Modeling Approach

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters

scholarly journals Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Jacob M. Remington ◽  
Jonathon B. Ferrell ◽  
Marlo Zorman ◽  
Adam Petrucci ◽  
Severin T. Schneebeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Student Learning ◽  
Molecular Properties ◽  
Computer Hardware ◽  
Data Driven ◽  
Teaching Approaches ◽  
Advanced Level ◽  
Interdisciplinary Field ◽  
Elective Course

ABSTRACT Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

Sign in / Sign up

Export Citation Format

Share Document