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Active Learning Applications

Accelerate discovery with machine learning

Amplify discovery across vast chemical space

Amplify discovery across vast chemical space

Active Learning Applications is a powerful tool that trains a machine learning (ML) model on physics-based data, such as FEP+ predicted affinities or Glide docking scores, iteratively sampled from a full library.

Trained models can rapidly generate predictions for new molecules and identify the highest-scoring compounds in ultra-large libraries at a fraction of the cost and speed of brute force methods.

Key applications across drug discovery

Active Learning Glide
Find potent hits in ultra-large libraries

Screen billions of compounds with Glide docking amplified by cutting-edge machine learning models in a fraction of the time. Use Active Learning to recover ~70% of the same top-scoring hits that would have been found from brute force docking of ultra-large libraries with Glide, for only 0.1% of the cost.

Active Learning FEP+
Explore diverse chemical space in lead optimization

Explore tens of thousands to hundred of thousands of idea compounds with Active Learning FEP+, against multiple hypotheses simultaneously, to quickly identify compounds that maintain or improve potency while achieving other design objectives.

FEP Protocol Builder
Expedite FEP+ use for challenging systems with a fully automated workflow

Rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP Protocol Builder uses an Active Learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols, saving researcher time and increases the chances of successfully enabling FEP+.

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De Novo Design Workflow

Schrödinger’s De Novo Design Workflow is a fully-integrated, cloud-based design system for ultra-large scale chemical space exploration and refinement.

Starting from a hit molecule or lead series, the technology identifies synthetically tractable molecules that meet key project criteria by combining multiple compound enumeration strategies with an advanced filtering cascade (AutoDesigner) and rigorous potency scoring with free energy calculations (Active Learning FEP+).

Related Products

Learn more about the related computational technologies available to progress your research projects.


Industry-leading ligand-receptor docking solution


High-performance free energy calculations for drug discovery

De Novo Design Workflow

Fully-integrated, cloud-based design system for ultra-large scale chemical space exploration and refinement

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

Life Science
Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schr’dinger Computational Platform
Life Science
Impacting Drug Discovery Projects with Large-Scale Enumerations, Machine Learning Strategies, and Free-Energy Predictions
Life Science
AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of D-Amino Acid Oxidase Inhibitors
Life Science
Efficient Exploration of Chemical Space with Docking and Deep-Learning

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.


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.