The gold-standard computational platform combining physics-based modeling and machine learning
Schrödinger is the premier enterprise platform for drug discovery, integrating rigorous free energy perturbation (FEP+), molecular docking (Glide), and machine learning models for hit identification and lead optimization.
Industry-leading FEP+ for predicting relative and absolute protein-ligand binding free energiesGlide docking engine with induced-fit and deep-learning scoring optionsMaestro 3D visual modeling and pharmacophore hypothesis generationActive learning molecular property prediction across multi-billion compound spaces
Schrödinger is the premier enterprise platform for drug discovery, integrating rigorous free energy perturbation (FEP+), molecular docking (Glide), and machine learning models for hit identification and lead optimization.
Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:09:40
Key Features
Industry-leading FEP+ for predicting relative and absolute protein-ligand binding free energies
Glide docking engine with induced-fit and deep-learning scoring options
Maestro 3D visual modeling and pharmacophore hypothesis generation
Active learning molecular property prediction across multi-billion compound spaces
Interactive 3D Structure
Schrödinger Maestro & Computational Chemistry Suite Predicted Complex
Streams real 3D atomic coordinates from RCSB Protein Data Bank
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pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low
Academic Context & Research Evidence
Biological & Workflow Fit
Biological Application
Small-molecule drug discovery, structure-based design, and ADMET profiling
Research Workflow
Hit-to-lead and lead optimization via FEP+ calculations and docking
Use this citation format when referencing Schrödinger Maestro & Computational Chemistry Suite in scientific publications and benchmark papers.
@software{schrodinger_suite_2026,
title = {{Schrödinger Maestro & Computational Chemistry Suite}},
author = {{Schrödinger}},
year = {2026},
url = {https://www.schrodinger.com},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}
Peer-Reviewed Literature & Preprints
Live scientific citations streamed from Europe PMC and PubMed for Schrödinger Maestro & Computational Chemistry Suite.
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Technical / Product Information
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
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