Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset

2020 ◽  
Vol 124 (47) ◽  
pp. 9854-9866
Author(s):  
Gabriel A. Pinheiro ◽  
Johnatan Mucelini ◽  
Marinalva D. Soares ◽  
Ronaldo C. Prati ◽  
Juarez L. F. Da Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Molecular Properties

scholarly journals ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

2021 ◽  
Author(s):  
Hayley Weir ◽  
Keiran Thompson ◽  
Amelia Woodward ◽  
Benjamin Choi ◽  
Augustin Braun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Quantum Chemistry ◽  
Automated Recognition ◽  
Domain Expertise

Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline,...

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.

scholarly journals Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

2015 ◽  
Vol 11 (5) ◽  
pp. 2087-2096 ◽  
Author(s):  
Raghunathan Ramakrishnan ◽  
Pavlo O. Dral ◽  
Matthias Rupp ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Quantum Chemistry ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning

2019 ◽  
Vol 10 (35) ◽  
pp. 8154-8163 ◽  
Author(s):  
Yao Zhang ◽  
Alpha A. Lee
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Molecular Properties ◽  
Learning Models ◽  
Molecular Properties Prediction ◽  
Design Experiments ◽  
Machine Learning Models

We report a statistically principled method to quantify the uncertainty of machine learning models for molecular properties prediction. We show that this uncertainty estimate can be used to judiciously design experiments.

Constant size descriptors for accurate machine learning models of molecular properties

2018 ◽  
Vol 148 (24) ◽  
pp. 241718 ◽  
Author(s):  
Christopher R. Collins ◽  
Geoffrey J. Gordon ◽  
O. Anatole von Lilienfeld ◽  
David J. Yaron
Keyword(s):  
Machine Learning ◽  
Molecular Properties ◽  
Learning Models ◽  
Constant Size ◽  
Machine Learning Models
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