IFD-MD
Accurate ligand binding mode prediction for novel chemical matter
The domain of applicability of structure-based drug design (SBDD) is limited by the difficulty of obtaining a reliable receptor-ligand structure. The dominant method of obtaining a structure (x-ray crystallography) is both expensive and time consuming. In some cases, such as membrane proteins or GPCR’s, obtaining a structure would be difficult if not impossible.
IFD-MD is able to obtain an accurate structure to use in SBDD more quickly and easily than experimental determination.
Expand the domain of applicability of structure-based drug design with IFD-MD
IFD-MD utilizes pharmacophore docking to initially place the ligand while ignoring receptor clashes. This allows for conformational sampling of the receptor in the presence of a docked ligand, rather than refining an empty binding-site.
IFD-MD incorporates WScore to detect and penalize desolvation of polar groups caused by non-native ligand poses. In the example shown here, this incorrect ligand pose blocks a backbone NH and is therefore penalized.
Explicit MD waters are incorporated to provide a complete representation of the system. The placement and interaction with water forms an important part of the IFD-MD scoring function.
Benefits of IFD-MD for Drug Discovery

Rationalize Structure Activity Relationships

Enable Use of Homology Models in Drug Design

Generate Alternative Receptor Conformations for Virtual Screening

Jumpstart Lead Optimization from Patent Data

Predict and Visualize Bound Waters Crucial for Ligand Binding

Compare Alternative Binding Modes With an Intuitive Scoring Function

Rationalize HTS Hits

Understand Off-Target Activity

Explore FEP+ outliers
Overview of IFD-MD
Steps to create a valid ligand-receptor structure
Performance
In each experiment the binding pose was predicted starting with a holo structure of the target protein bound to a different ligand. The median tanimoto similarity of the holo-structure ligand to the predicted ligand is 0.04; the ligands were not congeneric. In over 90% of cases a pose within 2.5 Å ligand heavy-atom RMSD to the experimentally determined binding pose was within the top 2 poses predicted by IFD-MD. In over 80% of those cases the single top scoring pose was within 2.5 Å RMSD. For comparison the older IFD can identify a pose under 2.5 Å RMSD in the top 2 in only 60% of these cases and in the single top ranked pose in only 50% of cases.
Resources

Blog Post
THE NEW SOLUTION TO THE INDUCED FIT DOCKING PROBLEM
Dr. Ed Miller describes how next generation induced fit docking workflows can provides an accurate way to determine binding modes of novel ligand scaffolds.
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Science Article
IFD-MD App Note
IFD-MD takes the available structure and predicts atomic details of the protein-ligand complex structure needed for SBDD. See the details of how it performs in this application note.
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Blog Post
STORIES FROM DRUG DISCOVERY: Modeling Strategies in the Pursuit of Development Candidate in Oncology Program 1
Dr. Sayan Mondal, a Research Leader in Schrödinger’s Drug Discovery Group, demonstrates modeling strategies used from start to finish in a two-year oncology project.
ViewTraining Materials
Cross-Docking with IFD-MD
In this tutorial you will learn how to use IFD-MD to generate a predicted binding pose of a known active compound using a holo crystal structure solved with a different ligand as a starting point.
Expanding the Domain of Applicability with IFD-MD Webinar
An overview of IFD-MD and presentation of the results of 41 targets consisting of 415 cross-docks divided among a training and test set.
Publications

Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models
Tianchuan Xu, Kai Zhu, Alexandre Beautrait, Jeremie Vendome, Kenneth Borrelli, Robert Abel, Richard Friesner, Edward Miller

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding
Miller, E.; Murphy, R.; Sindhikara, D.; Borrelli, K.; Grisewood, M.; Ranalli, F.; Dixon, S.; Jerome, S.; Boyles, N.; Day, T.; Ghanakota, P.; Mondal, S.; Rafi, S.B.; Troast, D.M.; Abel, R.; Friesner, R.A.,