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Schrödinger's AutoQSAR for Machine Learning
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.
Schrödinger's AutoQSAR tool for Machine Learning
For free access on nanoHUB please see the Schrödinger Materials Science group for details.
Schrödinger AutoQSAR product information
Data-Driven Materials Innovation: where Machine Learning Meets Physics
Online Presentations | 29 Nov 2023 | Contributor(s):: Anand Chandrasekaran
Learn how Schrödinger’s tools can address common issues by using a combination of physics-based simulation data, enterprise informatics, and chemistry-aware ML.
Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR
Online Presentations | 29 Nov 2023 | Contributor(s):: Michael Rauch
In this workshop, we will demonstrate the hands-on use of Schrödinger's MS Maestro graphical user interface within nanoHUB to perform machine learning model creation and implementation.
Schrödinger Materials Science AutoQSAR for Machine Learning
Tools | 11 Sep 2023
Build quantitative structure-activity relationships (QSAR) automatically for molecular systems with Schrödinger's AutoQSAR tool