DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

DeepAutoQSAR

Create high-performing machine learning models using state-of-the-art methods

DeepAutoQSAR is a machine learning (ML) solution that allows users to predict molecular properties based on chemical structure. The automated supervised learning pipeline enables both novice and experienced users to train and inference best-in-class quantitative structure activity/property relationship (QSAR/QSPR) models.

Key Capabilities

Streamline model building with fully automated workflows

Automatically compute descriptors and fingerprints, create models with multiple machine learning architectures, and evaluate model performance.

Customize models to your project with unique project-specific descriptors

Provide your own descriptors in CSV format to be used in addition to or instead of those generated by DeepAutoQSAR for a wide range of applications beyond small molecules, such as polymers, organic electronics, catalysis, and more.

Ensure model optimization using best practices

Employ QSAR/QSPR best practices to minimize the likelihood of overfitting or misrepresenting a model’s performance while ensuring maximum predictive model performance.

Understand the domain of applicability using model confidence estimates

DeepAutoQSAR provides uncertainty estimates alongside model predictions to help determine how much confidence should be placed on predictions generated for candidate molecules which may lie beyond the model’s training set.

Visualize and analyze results to gain further insights 

Visualize color-coded atomic contributions towards target property facilitating ideation of novel chemistry. Visualize and analyze DeepAutoQSAR metrics reports and plots in Maestro to enable further experiments — quickly learn what model architectures are most effective and how models generalize on holdout sets.  

Scalable training to support small or large datasets

Use classical ML methods like boosted trees on smaller datasets while also supporting the largest scale QSAR/QSPR models using graph neural networks and other modern deep learning approaches.

Case studies & resources

Discover how Schrödinger technology is being used to solve real-world research challenges.

De novo design of hole-conducting molecules for organic electronics

Accelerating the design and optimization of OLED materials using active learning

Machine-learned force Fields for improved materials modeling

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Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science
Development of Scalable and Generalizable Machine Learned Force Field for Polymers
Materials Science
Design and Synthesis of Novel Oxime Ester Photoinitiators Augmented by Automated Machine Learning
Materials Science
Benchmarking Machine Learning Descriptors for Crystals
Materials Science
Machine Learning for the Design of Novel OLED Materials
Materials Science
Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics
Materials Science
Design of organic electronic materials with a goal-directed generative model powered by deep neural networks and high-throughput molecular simulations
Life Science
A Descriptor Set for Quantitative Structure-Property Relationship Prediction in Biologics
Life Science
Digitalisierung: molekulares Design plattformisieren
Life Science
Automated Protocol for Large-Scale Modeling of Gene Expression Data
Life Science
AutoQSAR: An Automated Machine Learning Tool for Best-Practice QSAR Modeling

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.