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Schrödinger Materials Science AutoQSAR for Machine Learning
Build quantitative structure-activity relationships (QSAR) automatically for molecular systems with Schrödinger's AutoQSAR tool
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Abstract
Welcome to Schrödinger's AutoQSAR for Machine Learning educational tool implemented in nanoHUB.
Schrödinger's Materials Science platform integrates predictive physics-based simulation with machine learning techniques to accelerate materials design. Our iterative process is designed to accelerate evaluation and optimization of chemical matter in silico ahead of synthesis and characterization. Promising compounds emerging from successive synthetic rounds can be optimized even further through additional computation cycles.
Various tutorials are available here in nanoHUB to allow educational use of Schrödinger's AutoQSAR tool for materials science. The AutoQSAR tool is for automated creation, validation and application of QSPR models following a best practices approach. The available tutorials demonstrate the use of AutoQSAR to build and rank order numerical QSPR models, visualize atomic contributions to property predictions and use these models to make predictions on new, unseen datasets.
Please find the tutorials under the Supporting Docs tab
To learn more about Schrödinger and other materials science products please visit the following:
Schrödinger's computational platform for materials: https://www.schrodinger.com/platform/materials-science
Schrödinger's Materials Science Maestro GUI: https://www.schrodinger.com/products/ms-maestro
Schrödinger's AutoQSAR platform: https://www.schrodinger.com/products/autoqsar
Schrödinger's quantum mechanics package Jaguar: https://www.schrodinger.com/products/ms-jaguar
Schrödinger's molecular dynamics simulations: https://www.schrodinger.com/products/desmond
In addition to the tool available here, 70+ tutorials and 7 online courses are available through Schrödinger directly. Please visit: https://www.schrodinger.com/learn/training/materials-science for more information
References
· "AutoQSAR: An Automated Machine Learning Tool for Best-Practice QSAR Modeling"
Dixon, S.L.; Duan, J.; Smith, E.; Von Bargen, C.D.; Sherman, W.; Repasky, M.P., Future Med. Chem., 2016, 8 (15), 1825-1839
Publications
· "Design and Synthesis of Novel Oxime Ester Photoinitiators Augmented by Automated Machine Learning"
Won Jung Lee, H. Shaun Kwak, Deuk-rak Lee, Chunrim Oh, Eul Kgun Yum, Yuling An, Mathew D. Halls, and Chi-Wan Lee, Chemistry of Materials, 2022, 34(1), 116-127
· "Machine Learning for the Design of Novel OLED Materials"
Hadi Abroshan, Paul Winget, H. Shaun Kwak, Yuling An, Christopher T. Brown, and Mathew D. Halls, Machine Learning in Materials Informatics: Methods and Applications, 2022, 1416, 33-49
· "Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics"
Hadi Abroshan, H. Shaun Kwak, Yuling An, Christopher Brown, Anand Chandrasekaran, Paul Winget, and Mathew D. Halls, Frontiers in Chemistry, 2022, ,
· "A Descriptor Set for Quantitative Structure-Property Relationship Prediction in Biologics"
Kannan Sankar, Kyle Trainor, Levi L. Blazer, Jarrett J. Adams, Sachdev S. Sidhu, Tyler Day, Elizabeth Meiering, Johannes K. X. Maier, Mol Inform, 2022, ,
· "Digitalisierung: molekulares Design plattformisieren"
Scarbath-Evers, K.; Cappel, D.; Weiser, J., Nachrichten aus der Chemie, 2020, Jul (68), 34-36
· "Automated Protocol for Large-Scale Modeling of Gene Expression Data"
Hall, M.L.; Calkins, D.; Sherman, W.B., J. Chem. Inf. Model., 2016, 56 (11), 2216–2224
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