Active Learning Applications
Accelerate research with machine learning
Modern, physics-based computational methods offer quantitative accuracy for a broad range of important properties for drug discovery and materials design. These methods are compute-intensive and often intractable at the scale of today's ultra-large chemical libraries. This classic accuracy vs. speed tradeoff requires scientists to make difficult choices in selecting a computational strategy which typically involves subsetting the data or resorting to faster (but less accurate) methods.
Active Learning technology from Schrödinger offers a solution to this problem. Active Learning workflows train a machine learning (ML) model on physics-based data, such as FEP+ predicted affinities or Glide docking scores, iteratively sampled from a full library using Schrödinger's deep-learning powered QSAR platform, DeepAutoQSAR. These models can generate predictions for new molecules much more rapidly, and can identify the highest-scoring compounds in a large library at a fraction of the cost. We have shown that these hybrid models’ speed to be comparable to a ligand-based method.
Accelerate research with machine learning using Schrödinger’s Active Learning applications
Benefits of Active Learning

Handle Large Libraries

Explore Diverse Chemical Space

Find Potent Hits

Explore Multiple Hypotheses

Get Results Faster
Active Learning Calculator
Glide (Dock All Compounds)
cost
Active Learning Glide
cost
For Compounds
Enter in the numbers for your project (type in the box or use slider) to compare compute time and cost.
*Estimated customer compute costs only, based on $0.06 per CPU hour and $.35 per GPU hour. Recommended hardware for AL-Learning Glide.
License costs are not included. Contact us for a quote.
Drive research in a multitude of project areas
Schrödinger currently has multiple software applications that can utilize Active Learning to accelerate the discovery process.
The following is a list of what is currently offered:
Resources
Hardware Requirements
Details of hardware and compute environment requirements to run Active Learning Glide.
High Performance Cloud Computing
Amplify the backend computing resources you need, when you need them, with the auto-scaling Virtual Clusters of Schrödinger in the Cloud.
Accelerating the Discovery of New Therapies to Fight COVID-19
As the biopharma industry races to develop treatments and vaccines for the global pandemic, it’s imperative that we both broaden our gaze and quicken our pace.
AI and Deep Learning in Drug Discovery and Materials Science
Schrödinger’s Machine Learning experts discuss AI and Deep Learning’s role in Drug Discovery and Materials Science now and in the future.
Discovery at Scale for Small-molecule Drug Discovery
Experts discuss the latest innovations in Small-molecule drug discovery at Schrödinger’s 2020 User Group Meeting.
Publications

Efficient Exploration of Chemical Space with Docking and Deep-Learning
Yang, Y.; Yao, K.; Repasky, M. P.; Leswing, K.; Abel, R.; Shoichet, B.; Jerome, S.

Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors
Konze, K.; Bos, P.; Dahlgren, M.; Leswing, K.; Tubert-Brohman, I.; Bortolato, A.; Robbason, B.; Abel, R.; Bhat, S.