Accurate ligand binding mode prediction for novel chemical matter, for on-targets and off-targets

Hit-to-Lead & Lead Optimization

Open new doors to structure-based design for a broader range of targets and off-targets

IFD-MD is a powerful GPU-accelerated solution for predicting receptor-ligand binding poses at an accuracy approaching experimental methods, but at a reduced cost and faster turnaround. Using IFD-MD in combination with FEP+ for model validation allows a full in silico method for deploying high precision structure-based drug discovery starting from homology models, AlphaFold structures, or experimental structures bound to unrelated chemical matter.

Advantages of IFD-MD for ligand-binding mode prediction

Easy-to-use graphical interface
Expedite programs without waiting to obtain an experimental structure

Progress structure-based design efforts without waiting for an experimental crystal structure of a new chemical series 

Explore binding sites to guide ligand design
Understand and de-risk off-target activities

Eliminate the need to initiate a new crystallization program of a known off-target bound to chemical matter

Customizable constraints
Applicable to a wide range of modalities

From non-covalent ligands to covalent ligands and macrocycles

Customizable constraints
Rationalize membrane protein targets

with explicit water molecules in the binding site and explicit lipid molecules in the membrane region

How it works

End checkpoint
Incorporates the effects that water molecules have on binding as an important component of the IFD-MD scoring function
End checkpoint
Detects and penalizes desolvation of polar groups caused by non-native ligand poses
End checkpoint
Utilizes a consensus mode to produce models that can help explain potential liabilities from common promiscuous off-targets
FeaturedCase Study Feature CDC7

Design of a novel, potent CDC7 inhibitor development candidate with high ligand efficiency and optimized properties

Learn how Schrödinger’s digital chemistry platform facilitates efficient multi-parameter optimization of selectivity, cell potency, and toxicity at scale.

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

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Structure–activity relationship of a pyrrole based series of PfPKG inhibitors as anti-malarials
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Enabling Structure-Based Drug Discovery Utilizing Predicted Models
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Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation
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Benchmark and Refinement of AlphaFold2 Structures for Hit Discovery
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Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors
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Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models
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Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding

Training & Resources

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